From e7975c7a8e2b25116a887dcfd53a76fc6feb5a8d Mon Sep 17 00:00:00 2001 From: Jan-Christoph Klie Date: Sun, 15 Nov 2020 01:11:52 +0100 Subject: [PATCH 1/5] No issue - Fix workshop papers --- templates/livestream.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/templates/livestream.html b/templates/livestream.html index 51d636f..c884c23 100644 --- a/templates/livestream.html +++ b/templates/livestream.html @@ -16,7 +16,7 @@

{% block content %} -{% set livestream_id = '38938634' %} +{% set livestream_id = '42' %} {% set cart_url = "https://www.streamtext.net/text.aspx?event=EMNLPCONFERENCE" %}
From 1880dca232e38bbbe89983dc7eb3190818c641cc Mon Sep 17 00:00:00 2001 From: Jan-Christoph Klie Date: Sun, 15 Nov 2020 02:09:56 +0100 Subject: [PATCH 2/5] No issue - Fix workshop papers --- scripts/dataentry/socials.py | 5 +- scripts/dataentry/workshops.py | 262 +++++-- sitedata/config.yml | 2 +- sitedata/socials.yml | 26 +- sitedata/workshop_papers.csv | 1334 ++++++++++++++++---------------- sitedata/workshops.yml | 34 +- templates/base.html | 7 +- templates/index.html | 3 + templates/paper.html | 2 +- 9 files changed, 935 insertions(+), 740 deletions(-) diff --git a/scripts/dataentry/socials.py b/scripts/dataentry/socials.py index f036702..8101375 100644 --- a/scripts/dataentry/socials.py +++ b/scripts/dataentry/socials.py @@ -37,7 +37,7 @@ def generate_socials(): ], ) df = df.dropna(subset=["ID"]) - df = df[:-1] + df = df.drop([df.index[-2]]) zoom_df = pd.read_excel( PATH_ZOOM_ACCOUNTS_WITH_PASSWORDS, sheet_name="Affinity" @@ -57,6 +57,7 @@ def generate_socials(): id_to_organizers = { row["ID"]: [e.strip() for e in row["Organizers"].split(",")] for _, row in df.iterrows() + if row["Organizers"] } id_to_name = {row["ID"]: row["Event"] for _, row in df.iterrows()} id_to_channel = {row["ID"]: row["Channel Name"] for _, row in df.iterrows()} @@ -122,7 +123,7 @@ def generate_socials(): for idx, row in df.iterrows(): name = "S-" + row["Session Name"].strip() - if uid.startswith("B") and row["Host"]: + if (uid.startswith("B") or uid.startswith("M")) and row["Host"]: name = name + " with " + row["Host"] day = row["Day"] diff --git a/scripts/dataentry/workshops.py b/scripts/dataentry/workshops.py index cb75f7d..0d97e38 100644 --- a/scripts/dataentry/workshops.py +++ b/scripts/dataentry/workshops.py @@ -1,27 +1,40 @@ -import csv from collections import defaultdict -from dataclasses import dataclass -import random -from typing import List, Dict, Any import re +from collections import defaultdict +from dataclasses import dataclass +from datetime import datetime, timedelta +from typing import Any +from typing import List, Dict +import xml.etree.ElementTree as ET +import pandas as pd +import pytz import ruamel from ftfy import fix_text from openpyxl import load_workbook from pylatexenc.latex2text import LatexNodes2Text from ruamel import yaml - -import numpy as np - -from datetime import datetime, time, timedelta - -import pandas as pd - -import pytz +from ruamel import yaml # https://docs.google.com/spreadsheets/d/19LRnJpae5NQd0D1NEO40kTbwDvS9f125tpsjBdevrcs/edit#gid=0 from scripts.dataentry.paths import * +fix = { + "490": "4th Workshop on Structured Prediction for NLP", + "510": "CoNLL 2020", + "884": "Deep Learning Inside Out (DeeLIO): The First Workshop on Knowledge Extraction and Integration for Deep Learning Architectures", + "1093": "SIGTYP 2020: The Second Workshop on Computational Research in Linguistic Typology", + "1761": "Search-Oriented Conversational AI (SCAI) 2", + "2217": "The Fourth Workshop on Online Abuse and Harms (WOAH) a.k.a. ALW", + "2487": "1st Workshop on Computational Approaches to Discourse", + "2575": "Workshop on Insights from Negative Results in NLP", + "2797": "Deep Learning Inside Out (DeeLIO): The First Workshop on Knowledge Extraction and Integration for Deep Learning Architectures", + "2800": "Deep Learning Inside Out (DeeLIO): The First Workshop on Knowledge Extraction and Integration for Deep Learning Architectures", + "2976": "BlackboxNLP 2020: Analyzing and interpreting neural networks for NLP", + "3476": "Interactive and Executable Semantic Parsing (Int-Ex)", + "3561": "BlackboxNLP 2020: Analyzing and interpreting neural networks for NLP", +} + @dataclass class Session: @@ -38,6 +51,17 @@ class Workshop: description: str +@dataclass +class Paper: + uid: str + title: str + authors: str + abstract: str + track: str + kind: str + link: str + + def load_workshop_overview_excel() -> pd.DataFrame: wb = load_workbook(PATH_WORKSHOPS_OVERVIEW) ws = wb.worksheets[0] @@ -132,7 +156,6 @@ def load_schedule(): for ws in wb.worksheets[4:]: workshop_id = ws["B2"].value assert workshop_id.startswith("WS-"), "Does not start with WS: " + workshop_id - print(workshop_id, ws.title) description = ws["B3"].value or "" ws.delete_rows(1, 6) @@ -205,35 +228,9 @@ def load_slideslive(): workshop_df = load_workshop_overview_excel() - fix = { - "490": "4th Workshop on Structured Prediction for NLP", - "510": "CoNLL 2020", - "884": "Deep Learning Inside Out (DeeLIO): The First Workshop on Knowledge Extraction and Integration for Deep Learning Architectures", - "1093": "SIGTYP 2020: The Second Workshop on Computational Research in Linguistic Typology", - "1761": "Search-Oriented Conversational AI (SCAI) 2", - "2217": "The Fourth Workshop on Online Abuse and Harms (WOAH) a.k.a. ALW", - "2487": "1st Workshop on Computational Approaches to Discourse", - "2575": "Workshop on Insights from Negative Results in NLP", - "2797": "Deep Learning Inside Out (DeeLIO): The First Workshop on Knowledge Extraction and Integration for Deep Learning Architectures", - "2800": "Deep Learning Inside Out (DeeLIO): The First Workshop on Knowledge Extraction and Integration for Deep Learning Architectures", - "2976": "BlackboxNLP 2020: Analyzing and interpreting neural networks for NLP", - "3476": "Interactive and Executable Semantic Parsing (Int-Ex)", - "3561": "BlackboxNLP 2020: Analyzing and interpreting neural networks for NLP", - } - ws_name_to_id = { row["Name"]: row["UID"].strip() for _, row in workshop_df.iterrows() } - corrected_venues = [] - for _, row in df.iterrows(): - venue_id = row["Organizer track name"] - if row["Unique ID"].strip() in fix: - correct_venue_name = fix[row["Unique ID"]] - venue_id = ws_name_to_id[correct_venue_name] - - corrected_venues.append(venue_id) - - df["Organizer track name"] = corrected_venues return df @@ -251,44 +248,203 @@ def generate_workshop_papers(slideslive: pd.DataFrame): title = row["Title"].replace("\n", " ") title = LatexNodes2Text().latex_to_text(title) - title = fix_text(title) - author_list = [fix_text(e.strip()) for e in re.split(",| and | And ", row["Speakers"])] + title = fix_text(title).strip() + author_list = [ + fix_text(e.strip()) for e in re.split(",| and | And ", row["Speakers"]) + ] ws = row["Organizer track name"].strip() uid = row["Unique ID"].strip() + + print(ws, uid) + if ws == "WS-15" and str(uid) in fix.keys(): + continue + venues.append(ws) UIDs.append(f"{ws}.{uid}") titles.append(title) - authors.append( - "|".join(author_list) - ) + authors.append("|".join(author_list)) presentation_ids.append( row["SlidesLive link"].replace("https://slideslive.com/", "") ) + anthology_papers = get_anthology_workshop_papers() + title_to_anthology_paper = {a.title.strip().lower(): a for a in anthology_papers} + author_to_anthology_paper = {a.authors.lower(): a for a in anthology_papers} + + unmatched = [] + uid_to_anthology_paper = {} + for uid, title, author in zip(UIDs, titles, authors): + if uid.startswith(("WS-2")): + continue + + if title.lower() in title_to_anthology_paper: + assert uid not in uid_to_anthology_paper + uid_to_anthology_paper[uid] = title_to_anthology_paper[title.lower()] + else: + unmatched.append((uid, title, author.lower())) + + for uid, title, author in list(unmatched): + if author.lower() in author_to_anthology_paper: + assert uid not in uid_to_anthology_paper, ( + uid, + title, + author, + uid_to_anthology_paper[uid], + ) + uid_to_anthology_paper[uid] = author_to_anthology_paper[author.lower()] + unmatched.remove((uid, title, author.lower())) + + for e in unmatched: + print(e) + + print(len(unmatched), len(uid_to_anthology_paper)) + + abstracts = [] + urls = [] + for uid in UIDs: + if uid in uid_to_anthology_paper: + paper = uid_to_anthology_paper[uid] + abstracts.append(paper.abstract) + urls.append(paper.link) + else: + abstracts.append("") + urls.append("") + data = { "workshop": venues, "UID": UIDs, "title": titles, "authors": authors, + "abstract": abstracts, "presentation_id": presentation_ids, + "pdf_url": urls, } - columns = ["workshop", "UID", "title", "authors", "presentation_id"] + columns = [ + "workshop", + "UID", + "title", + "authors", + "abstract", + "presentation_id", + "pdf_url", + ] df = pd.DataFrame(data, columns=columns) df = df.drop_duplicates(subset=["UID"]) df.to_csv(PATH_YAMLS / "workshop_papers.csv", index=False) +def get_anthology_workshop_papers() -> List[Paper]: + anthology = ( + Path( + r"C:\Users\klie\AppData\Roaming\JetBrains\PyCharm2020.2\scratches\emnlp\acl-anthology" + ) + / "data" + ) + + conference = "emnlp" + year = 2020 + + mapping = { + "2020.conll-1": "WS-1", + "2020.alw-1": "WS-17", + "2020.blackboxnlp-1": "WS-25", + "2020.clinicalnlp-1": "WS-12", + "2020.cmcl-1": "WS-5", + "2020.codi-1": "WS-16", + "2020.deelio-1": "WS-13", + "2020.eval4nlp-1": "WS-20", + "2020.insights-1": "WS-3", + "2020.intexsempar-1": "WS-6", + "2020.louhi-1": "WS-19", + "2020.nlpbt-1": "WS-23", + "2020.nlpcovid19-1": "WS-26", + "2020.nlpcss-1": "WS-18", + "2020.nlposs-1": "WS-9", + "2020.privatenlp-1": "WS-24", + "2020.scai-1": "WS-4", + "2020.sdp-1": "WS-7", + "2020.sigtyp-1": "WS-11", + "2020.splu-1": "WS-10", + "2020.spnlp-1": "WS-21", + "2020.sustainlp-1": "WS-15", + "2020.wnut-1": "WS-14", + "2020.findings-1": "findings", + } + + papers = [] + for venue in mapping.keys(): + if venue.endswith("-1"): + file_name = venue[:-2] + else: + file_name = venue + + path_to_xml = anthology / "xml" / f"{file_name}.xml" + tree = ET.parse(path_to_xml) + root = tree.getroot() + collection_id = root.attrib["id"] + + for volume in root.findall("volume"): + + volume_id = volume.attrib["id"] + + for paper in volume.findall("paper"): + paper_id = paper.attrib["id"] + title = "".join(paper.find("title").itertext()) + uid = f"{collection_id}-{volume_id}.{paper_id}" + authors = [ + " ".join(author.itertext()) for author in paper.findall("author") + ] + authors = "|".join(authors) + + if paper.find("abstract"): + abstract = "".join(paper.find("abstract").itertext()) + else: + abstract = "" + + link = f"https://www.aclweb.org/anthology/{uid}" + + track = mapping[venue] + kind = None + + if track.startswith("W"): + kind = "workshop" + elif track == "main": + kind = "long" + else: + kind = "findings" + + assert kind + + paper = Paper( + uid=uid, + title=title, + authors=authors, + abstract=abstract, + track=track, + kind=kind, + link=link, + ) + + papers.append(paper) + + return papers + + def is_not_paper(row) -> bool: uid = row["Unique ID"].lower() title = row["Title"].lower() + return ( - "invited" in uid - or "challenge" in uid - or "invited" in title - or row["Unique ID"] == "Shared task" + ("invited" in uid) + or ("challenge" in uid) + or ("invited" in title) + or ("keynote" in title) + or ("keynote" in uid) + or (row["Unique ID"] == "Shared task") + or (title == "tba" and "paper" not in uid) ) @@ -325,9 +481,9 @@ def get_zooms() -> Dict[str, List[str]]: if __name__ == "__main__": - #download_slideslive() - #download_workshops() - #download_zooms() + # download_slideslive() + # download_workshops() + # download_zooms() # load_csv() data = build_workshops_basics() @@ -346,7 +502,7 @@ def get_zooms() -> Dict[str, List[str]]: uid = ws["UID"] ws["prerecorded_talks"] = talks[uid] - yaml.scalarstring.walk_tree(data) + yaml.scalarstring.walk_tree(data) with open(PATH_YAMLS / "workshops.yml", "w") as f: yaml.dump(data, f, Dumper=ruamel.yaml.RoundTripDumper) diff --git a/sitedata/config.yml b/sitedata/config.yml index 0a30f50..f8a2d03 100644 --- a/sitedata/config.yml +++ b/sitedata/config.yml @@ -23,7 +23,7 @@ default_presentation_id: 38931484 # Remove or change to your chat server. chat_server: emnlp2020.rocket.chat -gather_town: https://gather.town/ +gather_town: https://www.virtualchair.net/events/emnlp2020 connected_papers_conference_name: EMNLP2020 diff --git a/sitedata/socials.yml b/sitedata/socials.yml index 9df6836..df87472 100644 --- a/sitedata/socials.yml +++ b/sitedata/socials.yml @@ -141,8 +141,8 @@ start_time: 2020-11-17 20:00:00+00:00 end_time: 2020-11-17 21:00:00+00:00 - name: S-Session 2 - start_time: 2020-11-18 22:00:00+00:00 - end_time: 2020-11-18 23:00:00+00:00 + start_time: 2020-11-17 22:00:00+00:00 + end_time: 2020-11-17 23:00:00+00:00 - name: S-Session 3 start_time: 2020-11-18 10:00:00+00:00 end_time: 2020-11-18 11:00:00+00:00 @@ -426,6 +426,28 @@ - name: S-Session 2 with Luke Zettlemoyer start_time: 2020-11-18 05:00:00+00:00 end_time: 2020-11-18 06:00:00+00:00 +- name: Open Mentoring sessions + description: Mentoring sessions on various topics, open to everyone. Do note that + there is a capacity limit of 300. + UID: M2 + organizers: + members: + - Jack Hessel + - Tim Baldwin + - Ritwik Banerjee + - Steven Bethard + - Michael Roth + - Yevgeni Berzak + - Marzieh Fadaee + rocketchat_channel: + location: Zoom + sessions: + - name: S-Session 1 with Jack Hessel, Tim Baldwin, Ritwik Banerjee, Steven Bethard + start_time: 2020-11-17 04:00:00+00:00 + end_time: 2020-11-17 05:00:00+00:00 + - name: S-Session 2 with Michael Roth, Yevgeni Berzak, Marzieh Fadaee + start_time: 2020-11-17 12:00:00+00:00 + end_time: 2020-11-17 13:00:00+00:00 - name: Undergraduate Student Panel description: A panel designed for undergraduates attending the conference. We assume that these participants may be interested in pursuing a graduate degree, so we diff --git a/sitedata/workshop_papers.csv b/sitedata/workshop_papers.csv index 551ed37..9a685e1 100644 --- a/sitedata/workshop_papers.csv +++ b/sitedata/workshop_papers.csv @@ -1,677 +1,659 @@ -workshop,UID,title,authors,presentation_id -WS-1,WS-1.100,Modelling Lexical Ambiguity with Density Matrices,Francois Meyer|Martha Lewis,38939483 -WS-1,WS-1.101,Catplayinginthesnow: Impact of Prior Segmentation on a Model of Visually Grounded Speech,William Havard|Laurent Besacier|Jean-Pierre Chevrot,38939484 -WS-1,WS-1.108,Learning to ground medical text in a 3D human atlas,Dusan Grujicic|Gorjan Radevski|Tinne Tuytelaars|Matthew Blaschko,38939485 -WS-1,WS-1.109,Representation Learning for Type-Driven Composition,Gijs Wijnholds|Mehrnoosh Sadrzadeh|Stephen Clark,38939486 -WS-1,WS-1.113,Word Representations Concentrate and This is Good News!,Romain Couillet|Yagmur Gizem Cinar|Eric Gaussier|Muhammad Imran,38939487 -WS-1,WS-1.115,"""LazImpa"": Lazy and Impatient neural agents learn to communicate efficiently",Mathieu Rita|Rahma Chaabouni|Emmanuel Dupoux,38939488 -WS-1,WS-1.1165_F,Investigating Transferability in Pretrained Language Models,Alex Tamkin|Trisha Singh|Davide Giovanardi|Noah Goodman,38940643 -WS-1,WS-1.127,Re-solve it: simulating the acquisition of core semantic competences from small data,Aurélie Herbelot,38939489 -WS-1,WS-1.128,In Media Res: A Corpus for Evaluating Named Entity Linking with Creative Works,Adrian M.P. Brasoveanu|Albert Weichselbraun|Lyndon Nixon,38939490 -WS-1,WS-1.136,Analogies minus analogy test: measuring regularities in word embeddings,Louis Fournier|Emmanuel Dupoux|Ewan Dunbar,38939491 -WS-1,WS-1.137,Word associations and the distance properties of context-aware word embeddings,Maria Andueza Rodriguez|Paola Merlo,38939492 -WS-1,WS-1.14,Neural Proof Nets,Konstantinos Kogkalidis|Michael Moortgat|Richard Moot,38939465 -WS-1,WS-1.142,TrClaim-19: The First Collection for Turkish Check-Worthy Claim Detection with Annotator Rationales,Yavuz Selim Kartal|Mucahid Kutlu,38939493 -WS-1,WS-1.144,Discourse structure interacts with reference but not syntax in neural language models,Forrest Davis|Marten Van Schijndel,38939494 -WS-1,WS-1.147,Continual Adaptation for Efficient Machine Communication,Robert Hawkins|Minae Kwon|Dorsa Sadigh|Noah Goodman,38939495 -WS-1,WS-1.149,Diverse and Relevant Visual Storytelling with Scene Graph Embeddings,Xudong Hong|Rakshith Shetty|Asad Sayeed|Khushboo Mehra|Vera Demberg|Bernt Schiele,38939496 -WS-1,WS-1.15,TaxiNLI: Taking a Ride up the NLU Hill,Pratik Joshi|Somak Aditya|Aalok Sathe|Monojit Choudhury,38939466 -WS-1,WS-1.152,Alleviating Digitization Errors in Named Entity Recognition for Historical Documents,Emanuela Boros|Ahmed Hamdi|Elvys Linhares Pontes|Luis Adrián Cabrera-Diego|Jose G. Moreno|Nicolas Sidere|Antoine Doucet,38939497 -WS-1,WS-1.155,Analysing Word Representation from the Input and Output Embeddings in Neural Network Language Models,Steven Derby|Paul Miller|Barry Devereux,38939498 -WS-1,WS-1.156,On the Computational Power of Transformers and Its Implications in Sequence Modeling,Satwik Bhattamishra|Arkil Patel|Navin Goyal,38939499 -WS-1,WS-1.1597_F,Pragmatic Issue-Sensitive Image Captioning,Allen Nie|Reuben Cohn-Gordon|Christopher Potts,38940644 -WS-1,WS-1.162,An Expectation Maximisation Algorithm for Automated Cognate Detection,Roddy MacSween|Andrew Caines,38939500 -WS-1,WS-1.168,Filler-gaps that neural networks fail to generalize,Debasmita Bhattacharya|Marten Van Schijndel,38939501 -WS-1,WS-1.177,"Don't Parse, Insert: Multilingual Semantic Parsing with Insertion Based Decoding",Qile Zhu|Haidar Khan|Saleh Soltan|Stephen Rawls|Wael Hamza,38939502 -WS-1,WS-1.18,Modeling Subjective Assessments of Guilt in Newspaper Crime Narratives,Elisa Kreiss|Zijian Wang|Christopher Potts,38939467 -WS-1,WS-1.183,Learning Context-free Languages with Nondeterministic Stack RNNs,Brian DuSell|David Chiang,38939503 -WS-1,WS-1.185,Generating Narrative Text in a Switching Dynamical System,Noah Weber|Leena Shekhar|Heeyoung Kwon|Niranjan Balasubramanian|Nathanael Chambers,38939504 -WS-1,WS-1.189,What Are You Trying to Do? Semantic Typing of Event Processes,Muhao Chen|Hongming Zhang|Haoyu Wang|Dan Roth,38939505 -WS-1,WS-1.195,A Corpus for Outbreak Detection of Diseases Prevalent in Latin America,Antonella Dellanzo|Viviana Cotik|Jose Ochoa-Luna,38939506 -WS-1,WS-1.202,Are Pretrained Language Models Symbolic Reasoners over Knowledge?,Nora Kassner|Benno Krojer|Hinrich Schütze,38939507 -WS-1,WS-1.21,On the Frailty of Universal POS Tags for Neural UD Parsers,Mark Anderson|Carlos Gómez-Rodríguez,38939468 -WS-1,WS-1.218,Understanding Linguistic Accommodation in Code-Switched Human-Machine Dialogues,Tanmay Parekh|Emily Ahn|Yulia Tsvetkov|Alan W Black,38939508 -WS-1,WS-1.221,Identifying robust markers of Parkinson's disease in typing behaviour using a CNN-LSTM network,Neil Dhir|Mathias Edman|Álvaro Sanchez Ferro|Tom Stafford|Colin Bannard,38939509 -WS-1,WS-1.222,An Empirical Study on Model-agnostic Debiasing Strategies for Robust Natural Language Inference,Tianyu Liu|Zheng Xin|Xiaoan Ding|Baobao Chang|Zhifang Sui,38939510 -WS-1,WS-1.226,Cloze Distillation Improves Psychometric Predictive Power,Tiwalayo Eisape|Noga Zaslavsky|Roger Levy,38939511 -WS-1,WS-1.234,Disentangling dialects: a neural approach to Indo-Aryan historical phonology and subgrouping,Chundra Cathcart|Taraka Rama,38939512 -WS-1,WS-1.247,"A Dataset for Linguistic Understanding, Visual Evaluation, and Recognition of Sign Languages: The K-RSL",Alfarabi Imashev|Medet Mukushev|Vadim Kimmelman|Anara Sandygulova,38939513 -WS-1,WS-1.258,From Dataset Recycling to Multi-Property Extraction and Beyond,Tomasz Dwojak|Michał Pietruszka|Łukasz Borchmann|Jakub Chłędowski|Filip Graliński,38939514 -WS-1,WS-1.259,How well does surprisal explain N400 amplitude under different experimental conditions?,James Michaelov|Benjamin Bergen,38939515 -WS-1,WS-1.26,Classifying Syntactic Errors in Learner Language,Leshem Choshen|Dmitry Nikolaev|Yevgeni Berzak|Omri Abend,38939469 -WS-1,WS-1.28,How to Probe Sentence Embeddings in Low-Resource Languages: On Structural Design Choices for Probing Task Evaluation,Steffen Eger|Johannes Daxenberger|Iryna Gurevych,38939470 -WS-1,WS-1.29,Understanding the Source of Semantic Regularities in Word Embeddings,Hsiao-Yu Chiang|Jose Camacho-Collados|Zachary Pardos,38939471 -WS-1,WS-1.38,Finding The Right One and Resolving it,Payal Khullar|Arghya Bhattacharya|Manish Shrivastava,38939472 -WS-1,WS-1.49,Bridging Information-Seeking Human Gaze and Machine Reading Comprehension,Jonathan Malmaud|Roger Levy|Yevgeni Berzak,38939473 -WS-1,WS-1.510_F,Dynamic Data Selection for Curriculum Learning via Ability Estimation,John P. Lalor|Hong Yu,38940641 -WS-1,WS-1.561_F,From Language to Language-ish: How Brain-Like is an LSTM's Representation of Nonsensical Language Stimuli?,Maryam Hashemzadeh|Greta Kaufeld|Martha White|Andrea E. Martin|Alona Fyshe,38940642 -WS-1,WS-1.59,A Corpus of Very Short Scientific Summaries,Yifan Chen|Tamara Polajnar|Colin Batchelor|Simone Teufel,38939474 -WS-1,WS-1.61,Recurrent babbling: evaluating the acquisition of grammar from limited input data,Ludovica Pannitto|Aurélie Herbelot,38939475 -WS-1,WS-1.63,Explaining the efficiency of communication: How communicators can reduce their computational burden through interaction,Jacqueline Van Arkel|Marieke Woensdregt|Mark Dingemanse|Mark Blokpoel,38939476 -WS-1,WS-1.69,Acquiring language from speech by learning to remember and predict,Cory Shain|Micha Elsner,38939477 -WS-1,WS-1.69_F,"Stay Hungry, Stay Focused: Generating Informative and Specific Questions in Information-Seeking Conversations",Peng Qi|Yuhao Zhang|Christopher D. Manning,38940640 -WS-1,WS-1.7,Enriching Word Embeddings with Temporal and Spatial Information,Hongyu Gong|Suma Bhat|Pramod Viswanath,38939463 -WS-1,WS-1.70,Identifying Incorrect Labels in the CoNLL-2003 Corpus,Frederick Reiss|Hong Xu|Bryan Cutler|Karthik Muthuraman|Zachary Eichenberger,38939478 -WS-1,WS-1.73,When is a bishop not like a rook? When it's like a rabbi! Multi-prototype BERT embeddings for estimating semantic relationships,Gabriella Chronis|Katrin Erk,38939479 -WS-1,WS-1.8,Interpreting Attention Models with Human Visual Attention in Machine Reading Comprehension,Ekta Sood|Simon Tannert|Diego Frassinelli|Andreas Bulling|Ngoc Thang Vu,38939464 -WS-1,WS-1.83,Processing effort is a poor predictor of cross-linguistic word order frequency,Brennan Gonering|Emily Morgan,38939480 -WS-1,WS-1.88,Relations between comprehensibility and adequacy errors in machine translation output,Maja Popović,38939481 -WS-1,WS-1.96,Cross-lingual Embeddings Reveal Universal and Lineage-Specific Patterns in Grammatical Gender Assignment,Hartger Veeman|Marc Allassonnière-Tang|Aleksandrs Berdicevskis|Ali Basirat,38939482 -WS-1,WS-1.Shared1,Hitachi at MRP 2020: Text-to-Graph-Notation Transducer,Hiroaki Ozaki|Gaku Morio|Yuta Koreeda|Terufumi Morishita|Toshinori Miyoshi,38941228 -WS-1,WS-1.Shared2,HIT-SCIR at MRP 2020: Transition-based Parser and Iterative Inference Parser,Longxu Dou|Yunlong Feng|Yuqiu Ji|Wanxiang Che|Ting Liu,38941229 -WS-1,WS-1.Shared3,HUJI-KU at MRP 2020: Two Transition-based Neural Parsers,Ofir Arviv|Ruixiang Cui|Daniel Hershcovich,38941230 -WS-1,WS-1.Shared4,JBNU at MRP 2020: AMR Parsing Using a Joint State Model for Graph-Sequence Iterative Inference,Seung-Hoon Na|Jinwoo Min,38941231 -WS-1,WS-1.Shared5,ÚFAL at MRP 2020: Permutation-invariant Semantic Parsing in PERIN,David Samuel|Milan Straka,38941232 -WS-1,WS-1.Shared6,FGD at MRP 2020: Prague Tectogrammatical Graphs,Daniel Zeman|Jan Hajic,38941233 -WS-1,WS-1.Shared7,DRS at MRP 2020: Dressing up Discourse Representation Structures as Graphs,Lasha Abzianidze|Johan Bos|Stephan Oepen,38941234 -WS-1,WS-1.Shared8,MRP 2020: The Second Shared Task on Cross-Framework and Cross-Linguistic Meaning Representation Parsing,Stephan Oepen|Omri Abend|Lasha Abzianidze|Johan Bos|Jan Hajic|Daniel Hershcovich|Bin Li|Tim O'Gorman|Nianwen Xue|Daniel Zeman,38941235 -WS-10,WS-10.11,Geocoding with multi-level loss for spatial language representation,Sayali Kulkarni|Shailee Jain|Mohammad Javad Hosseini|Jason Baldridge|Eugene Ie|Li Zhang,38940083 -WS-10,WS-10.12,SpaRTQA: A Textual Question Answering Benchmark for Spatial Reasoning,Roshanak Mirzaee|Hossein Rajaby Faghihi|Parisa Kordjamshidi,38940084 -WS-10,WS-10.13,Vision-and-Language Navigation by Reasoning over Spatial Configurations,Yue Zhang|Quan Guo|Parisa Kordjamshidi,38940085 -WS-10,WS-10.1453,RMM: A Recursive Mental Model for Dialogue Navigation,Homero Roman Roman|Yonatan Bisk|Jesse Thomason|Asli Celikyilmaz|Jianfeng Gao,38940095 -WS-10,WS-10.1595,Robust and Interpretable Grounding of Spatial References with Relation Networks,Tsung-Yen Yang|Andrew S. Lan|Karthik Narasimhan,38940094 -WS-10,WS-10.2,They are not all alike: answering different spatial questions requires different grounding strategies,Alberto Testoni|Claudio Greco|Tobias Bianchi|Mauricio Mazuecos|Agata Marcante|Luciana Benotti|Raffaella Bernardi,38940076 -WS-10,WS-10.2904,ARRAMON: A Joint Navigation-Assembly Instruction Interpretation Task in Dynamic Environments,Hyounghun Kim|Abhay Zala|Graham Burri|Hao Tan|Mohit Bansal,38940093 -WS-10,WS-10.3,"Categorisation, Typicality Object-Specific Features in Spatial Referring Expressions",Adam Richard-Bollans|Anthony Cohn|Lucía Gómez Álvarez,38940077 -WS-10,WS-10.3302,Visually-Grounded Planning without Vision: Language Models Infer Detailed Plans from High-level Instructions,Peter A. Jansen,38940098 -WS-10,WS-10.3382,Decoding Language Spatial Relations to 2D Spatial Arrangements,Gorjan Radevski|Guillem Collell|Marie-Francine Moens|Tinne Tuytelaars,38940092 -WS-10,WS-10.3466,Language-Conditioned Feature Pyramids for Visual Selection Tasks,Taichi Iki|Akiko Aizawa,38940091 -WS-10,WS-10.5,BERT-based Spatial Information Extraction,Hyeong Jin Shin|Jeong Yeon Park|Dae Bum Yuk|Jae Sung Lee,38940078 -WS-10,WS-10.6,A Cognitively Motivated Approach to Spatial Information Extraction,Chao Xu|Emmanuelle-Anna Dietz Saldanha|Dagmar Gromann|Beihai Zhou,38940079 -WS-10,WS-10.676,A Linguistic Analysis of Visually Grounded Dialogues Based on Spatial Expressions,Takuma Udagawa|Takato Yamazaki|Akiko Aizawa,38940097 -WS-10,WS-10.7,A Hybrid Deep Learning Approach for Spatial Trigger Extraction from Radiology Reports,Surabhi Datta|Kirk Roberts,38940080 -WS-10,WS-10.8,An Element-wise Visual-enhanced BiLSTM-CRF Model for Location Name Recognition,Takuya Komada|Takashi Inui,38940081 -WS-10,WS-10.857,LiMiT: The Literal Motion in Text Dataset,Irene Manotas|Ngoc Phuoc An Vo|Vadim Sheinin,38940096 -WS-10,WS-10.9,Retouchdown: Releasing Touchdown on StreetLearn as a Public Resource for Language Grounding Tasks in Street View,Harsh Mehta|Yoav Artzi|Jason Baldridge|Eugene Ie|Piotr Mirowski,38940082 -WS-11,WS-11.10,Multilingual BERT Learns Abstract Case Representations,Isabel Papadimitriou|Ethan A. Chi|Richard Futrell|Kyle Mahowald,38939802 -WS-11,WS-11.1093-WS11,"Parsing with Multilingual BERT, a Small Corpus, and a Small Treebank",Ethan C. Chau|Lucy H. Lin|Noah A. Smith,38940630 -WS-11,WS-11.11,Keyword Spotting: A quick-and-dirty method for extracting typological features of language from grammatical descriptions,Harald Hammarström,38939803 -WS-11,WS-11.12,Imputing typological values via phylogenetic inference,Gerhard Jäger,38939793 -WS-11,WS-11.13,DEmA: the Pavia Diachronic Emergence of Alignment database,Sonia Cristofaro|Guglielmo Inglese,38939804 -WS-11,WS-11.14,A dataset and metric to evaluate lexical extraction from parallel corpora,Barend Beekhuizen,38939805 -WS-11,WS-11.1409,SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddings,Philipp Dufter|Masoud Jalili Sabet|‪François Yvon|‪Hinrich Schütze‬,38940631 -WS-11,WS-11.16,The role of community size and network structure in shaping linguistic diversity: experimental evidence,Limor Raviv|Antje Meyer|Shiri Lev-Ari,38939806 -WS-11,WS-11.17,NUIG: Multitasking Self-attention based approach to SigTyp 2020 Shared Task,Chinmay Choudhary|Colm O'Riordan,38939794 -WS-11,WS-11.18,KMI-Panlingua-IITKGP @SIGTYP2020: Exploring Rules and Hybrid Systems for Automatic Prediction of Typological Features,Ritesh Kumar|Deepak Alok|Akanksha Bansal|Bornini Lahiri|Atul Kr. Ojha,38939795 -WS-11,WS-11.2,Information from Topic Contexts: The Prediction of Aspectual Coding of Verbs in Russian,Michael Richter|Tariq Yousef,38939796 -WS-11,WS-11.3,Uncovering Typological Context-Sensitive Features,Chiara Alzetta|Felice Dell'Orletta|Simonetta Montemagni|Giulia Venturi,38939797 -WS-11,WS-11.4,NEMO: Frequentist Inference Approach to Constrained Linguistic Typology Feature Prediction in SIGTYP 2020 Shared Task,Alexander Gutkin|Richard Sproat,38939791 -WS-11,WS-11.5,Towards Induction of Structured Phoneme Inventories,Alexander Gutkin|Martin Jansche|Lucy Skidmore,38939798 -WS-11,WS-11.6,Is Typology-Based Adaptation Effective for Multilingual Sequence Labelling?,Ahmet Üstün|Arianna Bisazza|Gosse Bouma|Gertjan Van Noord,38939799 -WS-11,WS-11.7,Predicting Typological Features inWALS using Language Embeddings and Conditional Probabilities: ÚFAL Submission to the SIGTYP 2020 Shared Task,Martin Vastl|Daniel Zeman|Rudolf Rosa,38939792 -WS-11,WS-11.8,SNACS Annotation of Case Markers and Adpositions in Hindi,Aryaman Arora|Nathan Schneider,38939800 -WS-11,WS-11.816,Cross-lingual Alignment Methods for Multilingual BERT: A Comparative Study,Saurabh Kulshreshtha|‪José Luis Redondo García|Ching-Yun Chang,38940629 -WS-11,WS-11.9,Multilingual Jointly Trained Acoustic and Written Word Embeddings,Yushi Hu|Shane Settle|Karen Livescu,38939801 -WS-12,WS-12.1,Dilated Convolutional Attention Network for Medical Code Assignment from Clinical Text,Shaoxiong Ji|Erik Cambria|Pekka Marttinen,38939807 -WS-12,WS-12.10,Information Extraction from Swedish Medical Prescriptions with Sig-Transformer Encoder,John Pougué Biyong|Bo Wang|Terry Lyons|Alejo Nevado-Holgado,38939813 -WS-12,WS-12.1041,Learning to Generate Clinically Coherent Chest X-Ray Reports,Justin Lovelace|Bobak Mortazavi,38940177 -WS-12,WS-12.11,How You Ask Matters: The Effect of Paraphrastic Questions to BERT Performance on a Clinical SQuAD Dataset,Sungrim,38939814 -WS-12,WS-12.12,Multiple Sclerosis Severity Classification From Clinical Text,Alister D'Costa|Stefan Denkovski|Michal Malyska|Sae Young Moon|Brandon Rufino|Zhen Yang|Taylor Killian|Marzyeh Ghassemi,38939815 -WS-12,WS-12.13,An Ensemble Approach to Automatic Structuring of Radiology Reports,Morteza Pourreza Shahri|Amir Tahmasebi|Bingyang Ye|Henghui Zhu|Javed Aslam|Timothy Ferris,38939816 -WS-12,WS-12.15,Various Levels of Representation for Predicting Stroke Prognosis using Text Records of Magnetic Resonance Imaging,Tak-Sung Heo|Chulho Kim|Jeong-Myeong Choi|Yeong-Seok Jeong|Yu-Seop Kim,38939817 -WS-12,WS-12.16,Relative and Incomplete Time Expression Anchoring for Clinical Text,Louise Dupuis|Nicol Bergou|Hegler Tissot|Sumithra Velupillai,38939818 -WS-12,WS-12.1640,Learning Visual-Semantic Embeddings for Reporting Abnormal Findings on Chest X-rays,Jianmo Ni|Chun-Nan Hsu|Amilcare Gentili|Julian McAuley,38940178 -WS-12,WS-12.17,MeDAL: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining,Zhi Wen|Xing Han Lu|Siva Reddy,38939819 -WS-12,WS-12.1713,Characterizing the Value of Information in Medical Notes,Chao-Chun Hsu|Shantanu Karnwal|Sendhil Mullainathan|Ziad Obermeyer|Chenhao Tan,38940179 -WS-12,WS-12.18,Evaluation of Transfer Learning for Adverse Drug Event (ADE) and Medication Entity Extraction,Sankaran Narayanan|Kaivalya Mannam|Sreeranga P Rajan|P Venkat Rangan,38939820 +workshop,UID,title,authors,abstract,presentation_id,pdf_url +WS-1,WS-1.100,Modelling Lexical Ambiguity with Density Matrices,Francois Meyer|Martha Lewis,,38939483,https://www.aclweb.org/anthology/2020.conll-1.21 +WS-1,WS-1.101,Catplayinginthesnow: Impact of Prior Segmentation on a Model of Visually Grounded Speech,William Havard|Laurent Besacier|Jean-Pierre Chevrot,,38939484,https://www.aclweb.org/anthology/2020.conll-1.22 +WS-1,WS-1.108,Learning to ground medical text in a 3D human atlas,Dusan Grujicic|Gorjan Radevski|Tinne Tuytelaars|Matthew Blaschko,,38939485,https://www.aclweb.org/anthology/2020.conll-1.23 +WS-1,WS-1.109,Representation Learning for Type-Driven Composition,Gijs Wijnholds|Mehrnoosh Sadrzadeh|Stephen Clark,,38939486,https://www.aclweb.org/anthology/2020.conll-1.24 +WS-1,WS-1.113,Word Representations Concentrate and This is Good News!,Romain Couillet|Yagmur Gizem Cinar|Eric Gaussier|Muhammad Imran,"This article establishes that, unlike the legacy tf*idf representation, recent natural language representations (word embedding vectors) tend to exhibit a so-called concentration of measure phenomenon, in the sense that, as the representation size p and database size n are both large, their behavior is similar to that of large dimensional Gaussian random vectors. This phenomenon may have important consequences as machine learning algorithms for natural language data could be amenable to improvement, thereby providing new theoretical insights into the field of natural language processing.",38939487,https://www.aclweb.org/anthology/2020.conll-1.25 +WS-1,WS-1.115,"""LazImpa"": Lazy and Impatient neural agents learn to communicate efficiently",Mathieu Rita|Rahma Chaabouni|Emmanuel Dupoux,,38939488,https://www.aclweb.org/anthology/2020.conll-1.26 +WS-1,WS-1.1165_F,Investigating Transferability in Pretrained Language Models,Alex Tamkin|Trisha Singh|Davide Giovanardi|Noah Goodman,,38940643,https://www.aclweb.org/anthology/2020.findings-emnlp.125 +WS-1,WS-1.127,Re-solve it: simulating the acquisition of core semantic competences from small data,Aurélie Herbelot,,38939489,https://www.aclweb.org/anthology/2020.conll-1.27 +WS-1,WS-1.128,In Media Res: A Corpus for Evaluating Named Entity Linking with Creative Works,Adrian M.P. Brasoveanu|Albert Weichselbraun|Lyndon Nixon,,38939490,https://www.aclweb.org/anthology/2020.conll-1.28 +WS-1,WS-1.136,Analogies minus analogy test: measuring regularities in word embeddings,Louis Fournier|Emmanuel Dupoux|Ewan Dunbar,,38939491,https://www.aclweb.org/anthology/2020.conll-1.29 +WS-1,WS-1.137,Word associations and the distance properties of context-aware word embeddings,Maria Andueza Rodriguez|Paola Merlo,,38939492,https://www.aclweb.org/anthology/2020.conll-1.30 +WS-1,WS-1.14,Neural Proof Nets,Konstantinos Kogkalidis|Michael Moortgat|Richard Moot,,38939465,https://www.aclweb.org/anthology/2020.conll-1.3 +WS-1,WS-1.142,TrClaim-19: The First Collection for Turkish Check-Worthy Claim Detection with Annotator Rationales,Yavuz Selim Kartal|Mucahid Kutlu,,38939493,https://www.aclweb.org/anthology/2020.conll-1.31 +WS-1,WS-1.144,Discourse structure interacts with reference but not syntax in neural language models,Forrest Davis|Marten Van Schijndel,,38939494,https://www.aclweb.org/anthology/2020.conll-1.32 +WS-1,WS-1.147,Continual Adaptation for Efficient Machine Communication,Robert Hawkins|Minae Kwon|Dorsa Sadigh|Noah Goodman,,38939495,https://www.aclweb.org/anthology/2020.conll-1.33 +WS-1,WS-1.149,Diverse and Relevant Visual Storytelling with Scene Graph Embeddings,Xudong Hong|Rakshith Shetty|Asad Sayeed|Khushboo Mehra|Vera Demberg|Bernt Schiele,,38939496,https://www.aclweb.org/anthology/2020.conll-1.34 +WS-1,WS-1.15,TaxiNLI: Taking a Ride up the NLU Hill,Pratik Joshi|Somak Aditya|Aalok Sathe|Monojit Choudhury,,38939466,https://www.aclweb.org/anthology/2020.conll-1.4 +WS-1,WS-1.152,Alleviating Digitization Errors in Named Entity Recognition for Historical Documents,Emanuela Boros|Ahmed Hamdi|Elvys Linhares Pontes|Luis Adrián Cabrera-Diego|Jose G. Moreno|Nicolas Sidere|Antoine Doucet,,38939497,https://www.aclweb.org/anthology/2020.conll-1.35 +WS-1,WS-1.155,Analysing Word Representation from the Input and Output Embeddings in Neural Network Language Models,Steven Derby|Paul Miller|Barry Devereux,,38939498,https://www.aclweb.org/anthology/2020.conll-1.36 +WS-1,WS-1.156,On the Computational Power of Transformers and Its Implications in Sequence Modeling,Satwik Bhattamishra|Arkil Patel|Navin Goyal,,38939499,https://www.aclweb.org/anthology/2020.conll-1.37 +WS-1,WS-1.1597_F,Pragmatic Issue-Sensitive Image Captioning,Allen Nie|Reuben Cohn-Gordon|Christopher Potts,,38940644,https://www.aclweb.org/anthology/2020.findings-emnlp.173 +WS-1,WS-1.162,An Expectation Maximisation Algorithm for Automated Cognate Detection,Roddy MacSween|Andrew Caines,,38939500,https://www.aclweb.org/anthology/2020.conll-1.38 +WS-1,WS-1.168,Filler-gaps that neural networks fail to generalize,Debasmita Bhattacharya|Marten Van Schijndel,,38939501,https://www.aclweb.org/anthology/2020.conll-1.39 +WS-1,WS-1.177,"Don't Parse, Insert: Multilingual Semantic Parsing with Insertion Based Decoding",Qile Zhu|Haidar Khan|Saleh Soltan|Stephen Rawls|Wael Hamza,,38939502,https://www.aclweb.org/anthology/2020.conll-1.40 +WS-1,WS-1.18,Modeling Subjective Assessments of Guilt in Newspaper Crime Narratives,Elisa Kreiss|Zijian Wang|Christopher Potts,,38939467,https://www.aclweb.org/anthology/2020.conll-1.5 +WS-1,WS-1.183,Learning Context-free Languages with Nondeterministic Stack RNNs,Brian DuSell|David Chiang,,38939503,https://www.aclweb.org/anthology/2020.conll-1.41 +WS-1,WS-1.185,Generating Narrative Text in a Switching Dynamical System,Noah Weber|Leena Shekhar|Heeyoung Kwon|Niranjan Balasubramanian|Nathanael Chambers,,38939504,https://www.aclweb.org/anthology/2020.conll-1.42 +WS-1,WS-1.189,What Are You Trying to Do? Semantic Typing of Event Processes,Muhao Chen|Hongming Zhang|Haoyu Wang|Dan Roth,,38939505,https://www.aclweb.org/anthology/2020.conll-1.43 +WS-1,WS-1.195,A Corpus for Outbreak Detection of Diseases Prevalent in Latin America,Antonella Dellanzo|Viviana Cotik|Jose Ochoa-Luna,,38939506,https://www.aclweb.org/anthology/2020.conll-1.44 +WS-1,WS-1.202,Are Pretrained Language Models Symbolic Reasoners over Knowledge?,Nora Kassner|Benno Krojer|Hinrich Schütze,,38939507,https://www.aclweb.org/anthology/2020.conll-1.45 +WS-1,WS-1.21,On the Frailty of Universal POS Tags for Neural UD Parsers,Mark Anderson|Carlos Gómez-Rodríguez,,38939468,https://www.aclweb.org/anthology/2020.conll-1.6 +WS-1,WS-1.218,Understanding Linguistic Accommodation in Code-Switched Human-Machine Dialogues,Tanmay Parekh|Emily Ahn|Yulia Tsvetkov|Alan W Black,,38939508,https://www.aclweb.org/anthology/2020.conll-1.46 +WS-1,WS-1.221,Identifying robust markers of Parkinson's disease in typing behaviour using a CNN-LSTM network,Neil Dhir|Mathias Edman|Álvaro Sanchez Ferro|Tom Stafford|Colin Bannard,,38939509,https://www.aclweb.org/anthology/2020.conll-1.47 +WS-1,WS-1.222,An Empirical Study on Model-agnostic Debiasing Strategies for Robust Natural Language Inference,Tianyu Liu|Zheng Xin|Xiaoan Ding|Baobao Chang|Zhifang Sui,,38939510,https://www.aclweb.org/anthology/2020.conll-1.48 +WS-1,WS-1.226,Cloze Distillation Improves Psychometric Predictive Power,Tiwalayo Eisape|Noga Zaslavsky|Roger Levy,,38939511,https://www.aclweb.org/anthology/2020.conll-1.49 +WS-1,WS-1.234,Disentangling dialects: a neural approach to Indo-Aryan historical phonology and subgrouping,Chundra Cathcart|Taraka Rama,,38939512,https://www.aclweb.org/anthology/2020.conll-1.50 +WS-1,WS-1.247,"A Dataset for Linguistic Understanding, Visual Evaluation, and Recognition of Sign Languages: The K-RSL",Alfarabi Imashev|Medet Mukushev|Vadim Kimmelman|Anara Sandygulova,,38939513,https://www.aclweb.org/anthology/2020.conll-1.51 +WS-1,WS-1.258,From Dataset Recycling to Multi-Property Extraction and Beyond,Tomasz Dwojak|Michał Pietruszka|Łukasz Borchmann|Jakub Chłędowski|Filip Graliński,,38939514,https://www.aclweb.org/anthology/2020.conll-1.52 +WS-1,WS-1.259,How well does surprisal explain N400 amplitude under different experimental conditions?,James Michaelov|Benjamin Bergen,,38939515,https://www.aclweb.org/anthology/2020.conll-1.53 +WS-1,WS-1.26,Classifying Syntactic Errors in Learner Language,Leshem Choshen|Dmitry Nikolaev|Yevgeni Berzak|Omri Abend,,38939469,https://www.aclweb.org/anthology/2020.conll-1.7 +WS-1,WS-1.28,How to Probe Sentence Embeddings in Low-Resource Languages: On Structural Design Choices for Probing Task Evaluation,Steffen Eger|Johannes Daxenberger|Iryna Gurevych,,38939470,https://www.aclweb.org/anthology/2020.conll-1.8 +WS-1,WS-1.29,Understanding the Source of Semantic Regularities in Word Embeddings,Hsiao-Yu Chiang|Jose Camacho-Collados|Zachary Pardos,,38939471,https://www.aclweb.org/anthology/2020.conll-1.9 +WS-1,WS-1.38,Finding The Right One and Resolving it,Payal Khullar|Arghya Bhattacharya|Manish Shrivastava,,38939472,https://www.aclweb.org/anthology/2020.conll-1.10 +WS-1,WS-1.49,Bridging Information-Seeking Human Gaze and Machine Reading Comprehension,Jonathan Malmaud|Roger Levy|Yevgeni Berzak,,38939473,https://www.aclweb.org/anthology/2020.conll-1.11 +WS-1,WS-1.510_F,Dynamic Data Selection for Curriculum Learning via Ability Estimation,John P. Lalor|Hong Yu,,38940641,https://www.aclweb.org/anthology/2020.findings-emnlp.48 +WS-1,WS-1.561_F,From Language to Language-ish: How Brain-Like is an LSTM's Representation of Nonsensical Language Stimuli?,Maryam Hashemzadeh|Greta Kaufeld|Martha White|Andrea E. Martin|Alona Fyshe,,38940642,https://www.aclweb.org/anthology/2020.findings-emnlp.57 +WS-1,WS-1.59,A Corpus of Very Short Scientific Summaries,Yifan Chen|Tamara Polajnar|Colin Batchelor|Simone Teufel,,38939474,https://www.aclweb.org/anthology/2020.conll-1.12 +WS-1,WS-1.61,Recurrent babbling: evaluating the acquisition of grammar from limited input data,Ludovica Pannitto|Aurélie Herbelot,,38939475,https://www.aclweb.org/anthology/2020.conll-1.13 +WS-1,WS-1.63,Explaining the efficiency of communication: How communicators can reduce their computational burden through interaction,Jacqueline Van Arkel|Marieke Woensdregt|Mark Dingemanse|Mark Blokpoel,,38939476,https://www.aclweb.org/anthology/2020.conll-1.14 +WS-1,WS-1.69,Acquiring language from speech by learning to remember and predict,Cory Shain|Micha Elsner,,38939477,https://www.aclweb.org/anthology/2020.conll-1.15 +WS-1,WS-1.69_F,"Stay Hungry, Stay Focused: Generating Informative and Specific Questions in Information-Seeking Conversations",Peng Qi|Yuhao Zhang|Christopher D. Manning,,38940640,https://www.aclweb.org/anthology/2020.findings-emnlp.3 +WS-1,WS-1.7,Enriching Word Embeddings with Temporal and Spatial Information,Hongyu Gong|Suma Bhat|Pramod Viswanath,,38939463,https://www.aclweb.org/anthology/2020.conll-1.1 +WS-1,WS-1.70,Identifying Incorrect Labels in the CoNLL-2003 Corpus,Frederick Reiss|Hong Xu|Bryan Cutler|Karthik Muthuraman|Zachary Eichenberger,,38939478,https://www.aclweb.org/anthology/2020.conll-1.16 +WS-1,WS-1.73,When is a bishop not like a rook? When it's like a rabbi! Multi-prototype BERT embeddings for estimating semantic relationships,Gabriella Chronis|Katrin Erk,,38939479,https://www.aclweb.org/anthology/2020.conll-1.17 +WS-1,WS-1.8,Interpreting Attention Models with Human Visual Attention in Machine Reading Comprehension,Ekta Sood|Simon Tannert|Diego Frassinelli|Andreas Bulling|Ngoc Thang Vu,,38939464,https://www.aclweb.org/anthology/2020.conll-1.2 +WS-1,WS-1.83,Processing effort is a poor predictor of cross-linguistic word order frequency,Brennan Gonering|Emily Morgan,,38939480,https://www.aclweb.org/anthology/2020.conll-1.18 +WS-1,WS-1.88,Relations between comprehensibility and adequacy errors in machine translation output,Maja Popović,,38939481,https://www.aclweb.org/anthology/2020.conll-1.19 +WS-1,WS-1.96,Cross-lingual Embeddings Reveal Universal and Lineage-Specific Patterns in Grammatical Gender Assignment,Hartger Veeman|Marc Allassonnière-Tang|Aleksandrs Berdicevskis|Ali Basirat,,38939482,https://www.aclweb.org/anthology/2020.conll-1.20 +WS-1,WS-1.Shared1,Hitachi at MRP 2020: Text-to-Graph-Notation Transducer,Hiroaki Ozaki|Gaku Morio|Yuta Koreeda|Terufumi Morishita|Toshinori Miyoshi,,38941228,https://www.aclweb.org/anthology/2020.conll-shared.4 +WS-1,WS-1.Shared2,HIT-SCIR at MRP 2020: Transition-based Parser and Iterative Inference Parser,Longxu Dou|Yunlong Feng|Yuqiu Ji|Wanxiang Che|Ting Liu,,38941229,https://www.aclweb.org/anthology/2020.conll-shared.6 +WS-1,WS-1.Shared3,HUJI-KU at MRP 2020: Two Transition-based Neural Parsers,Ofir Arviv|Ruixiang Cui|Daniel Hershcovich,,38941230,https://www.aclweb.org/anthology/2020.conll-shared.7 +WS-1,WS-1.Shared4,JBNU at MRP 2020: AMR Parsing Using a Joint State Model for Graph-Sequence Iterative Inference,Seung-Hoon Na|Jinwoo Min,,38941231,https://www.aclweb.org/anthology/2020.conll-shared.8 +WS-1,WS-1.Shared5,ÚFAL at MRP 2020: Permutation-invariant Semantic Parsing in PERIN,David Samuel|Milan Straka,,38941232,https://www.aclweb.org/anthology/2020.conll-shared.5 +WS-1,WS-1.Shared6,FGD at MRP 2020: Prague Tectogrammatical Graphs,Daniel Zeman|Jan Hajic,,38941233,https://www.aclweb.org/anthology/2020.conll-shared.3 +WS-1,WS-1.Shared7,DRS at MRP 2020: Dressing up Discourse Representation Structures as Graphs,Lasha Abzianidze|Johan Bos|Stephan Oepen,,38941234,https://www.aclweb.org/anthology/2020.conll-shared.2 +WS-1,WS-1.Shared8,MRP 2020: The Second Shared Task on Cross-Framework and Cross-Linguistic Meaning Representation Parsing,Stephan Oepen|Omri Abend|Lasha Abzianidze|Johan Bos|Jan Hajic|Daniel Hershcovich|Bin Li|Tim O'Gorman|Nianwen Xue|Daniel Zeman,,38941235, +WS-10,WS-10.11,Geocoding with multi-level loss for spatial language representation,Sayali Kulkarni|Shailee Jain|Mohammad Javad Hosseini|Jason Baldridge|Eugene Ie|Li Zhang,,38940083, +WS-10,WS-10.12,SpaRTQA: A Textual Question Answering Benchmark for Spatial Reasoning,Roshanak Mirzaee|Hossein Rajaby Faghihi|Parisa Kordjamshidi,,38940084, +WS-10,WS-10.13,Vision-and-Language Navigation by Reasoning over Spatial Configurations,Yue Zhang|Quan Guo|Parisa Kordjamshidi,,38940085, +WS-10,WS-10.1453,RMM: A Recursive Mental Model for Dialogue Navigation,Homero Roman Roman|Yonatan Bisk|Jesse Thomason|Asli Celikyilmaz|Jianfeng Gao,,38940095,https://www.aclweb.org/anthology/2020.findings-emnlp.157 +WS-10,WS-10.1595,Robust and Interpretable Grounding of Spatial References with Relation Networks,Tsung-Yen Yang|Andrew S. Lan|Karthik Narasimhan,,38940094,https://www.aclweb.org/anthology/2020.findings-emnlp.172 +WS-10,WS-10.2,They are not all alike: answering different spatial questions requires different grounding strategies,Alberto Testoni|Claudio Greco|Tobias Bianchi|Mauricio Mazuecos|Agata Marcante|Luciana Benotti|Raffaella Bernardi,,38940076,https://www.aclweb.org/anthology/2020.splu-1.4 +WS-10,WS-10.2904,ARRAMON: A Joint Navigation-Assembly Instruction Interpretation Task in Dynamic Environments,Hyounghun Kim|Abhay Zala|Graham Burri|Hao Tan|Mohit Bansal,,38940093,https://www.aclweb.org/anthology/2020.findings-emnlp.348 +WS-10,WS-10.3,"Categorisation, Typicality Object-Specific Features in Spatial Referring Expressions",Adam Richard-Bollans|Anthony Cohn|Lucía Gómez Álvarez,,38940077,https://www.aclweb.org/anthology/2020.splu-1.5 +WS-10,WS-10.3302,Visually-Grounded Planning without Vision: Language Models Infer Detailed Plans from High-level Instructions,Peter A. Jansen,,38940098,https://www.aclweb.org/anthology/2020.findings-emnlp.395 +WS-10,WS-10.3382,Decoding Language Spatial Relations to 2D Spatial Arrangements,Gorjan Radevski|Guillem Collell|Marie-Francine Moens|Tinne Tuytelaars,,38940092,https://www.aclweb.org/anthology/2020.findings-emnlp.408 +WS-10,WS-10.3466,Language-Conditioned Feature Pyramids for Visual Selection Tasks,Taichi Iki|Akiko Aizawa,,38940091,https://www.aclweb.org/anthology/2020.findings-emnlp.420 +WS-10,WS-10.5,BERT-based Spatial Information Extraction,Hyeong Jin Shin|Jeong Yeon Park|Dae Bum Yuk|Jae Sung Lee,,38940078,https://www.aclweb.org/anthology/2020.splu-1.2 +WS-10,WS-10.6,A Cognitively Motivated Approach to Spatial Information Extraction,Chao Xu|Emmanuelle-Anna Dietz Saldanha|Dagmar Gromann|Beihai Zhou,,38940079,https://www.aclweb.org/anthology/2020.splu-1.3 +WS-10,WS-10.676,A Linguistic Analysis of Visually Grounded Dialogues Based on Spatial Expressions,Takuma Udagawa|Takato Yamazaki|Akiko Aizawa,,38940097,https://www.aclweb.org/anthology/2020.findings-emnlp.67 +WS-10,WS-10.7,A Hybrid Deep Learning Approach for Spatial Trigger Extraction from Radiology Reports,Surabhi Datta|Kirk Roberts,,38940080,https://www.aclweb.org/anthology/2020.splu-1.6 +WS-10,WS-10.8,An Element-wise Visual-enhanced BiLSTM-CRF Model for Location Name Recognition,Takuya Komada|Takashi Inui,,38940081,https://www.aclweb.org/anthology/2020.splu-1.1 +WS-10,WS-10.857,LiMiT: The Literal Motion in Text Dataset,Irene Manotas|Ngoc Phuoc An Vo|Vadim Sheinin,,38940096,https://www.aclweb.org/anthology/2020.findings-emnlp.88 +WS-10,WS-10.9,Retouchdown: Releasing Touchdown on StreetLearn as a Public Resource for Language Grounding Tasks in Street View,Harsh Mehta|Yoav Artzi|Jason Baldridge|Eugene Ie|Piotr Mirowski,,38940082,https://www.aclweb.org/anthology/2020.splu-1.7 +WS-11,WS-11.10,Multilingual BERT Learns Abstract Case Representations,Isabel Papadimitriou|Ethan A. Chi|Richard Futrell|Kyle Mahowald,,38939802, +WS-11,WS-11.1093-WS11,"Parsing with Multilingual BERT, a Small Corpus, and a Small Treebank",Ethan C. Chau|Lucy H. Lin|Noah A. Smith,,38940630,https://www.aclweb.org/anthology/2020.findings-emnlp.118 +WS-11,WS-11.11,Keyword Spotting: A quick-and-dirty method for extracting typological features of language from grammatical descriptions,Harald Hammarström,,38939803, +WS-11,WS-11.12,Imputing typological values via phylogenetic inference,Gerhard Jäger,,38939793,https://www.aclweb.org/anthology/2020.sigtyp-1.5 +WS-11,WS-11.13,DEmA: the Pavia Diachronic Emergence of Alignment database,Sonia Cristofaro|Guglielmo Inglese,,38939804, +WS-11,WS-11.14,A dataset and metric to evaluate lexical extraction from parallel corpora,Barend Beekhuizen,,38939805, +WS-11,WS-11.1409,SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddings,Philipp Dufter|Masoud Jalili Sabet|‪François Yvon|‪Hinrich Schütze‬,,38940631,https://www.aclweb.org/anthology/2020.findings-emnlp.147 +WS-11,WS-11.16,The role of community size and network structure in shaping linguistic diversity: experimental evidence,Limor Raviv|Antje Meyer|Shiri Lev-Ari,,38939806, +WS-11,WS-11.17,NUIG: Multitasking Self-attention based approach to SigTyp 2020 Shared Task,Chinmay Choudhary|Colm O'Riordan,"The paper describes the Multitasking Self-attention based approach to constrained sub-task within Sigtyp 2020 Shared task. Our model is simple neural network based architecture inspired by Transformers (CITATION) model. The model uses Multitasking to compute values of all WALS features for a given input language simultaneously. + +Results show that our approach performs at par with the baseline approaches, even though our proposed approach requires only phylogenetic and geographical attributes namely Longitude, Latitude, Genus-index, Family-index and Country-index and do not use any of the known WALS features of the respective input language, to compute its missing WALS features.",38939794,https://www.aclweb.org/anthology/2020.sigtyp-1.6 +WS-11,WS-11.18,KMI-Panlingua-IITKGP @SIGTYP2020: Exploring Rules and Hybrid Systems for Automatic Prediction of Typological Features,Ritesh Kumar|Deepak Alok|Akanksha Bansal|Bornini Lahiri|Atul Kr. Ojha,,38939795,https://www.aclweb.org/anthology/2020.sigtyp-1.2 +WS-11,WS-11.2,Information from Topic Contexts: The Prediction of Aspectual Coding of Verbs in Russian,Michael Richter|Tariq Yousef,,38939796, +WS-11,WS-11.3,Uncovering Typological Context-Sensitive Features,Chiara Alzetta|Felice Dell'Orletta|Simonetta Montemagni|Giulia Venturi,,38939797, +WS-11,WS-11.4,NEMO: Frequentist Inference Approach to Constrained Linguistic Typology Feature Prediction in SIGTYP 2020 Shared Task,Alexander Gutkin|Richard Sproat,,38939791,https://www.aclweb.org/anthology/2020.sigtyp-1.3 +WS-11,WS-11.5,Towards Induction of Structured Phoneme Inventories,Alexander Gutkin|Martin Jansche|Lucy Skidmore,,38939798, +WS-11,WS-11.6,Is Typology-Based Adaptation Effective for Multilingual Sequence Labelling?,Ahmet Üstün|Arianna Bisazza|Gosse Bouma|Gertjan Van Noord,,38939799, +WS-11,WS-11.7,Predicting Typological Features inWALS using Language Embeddings and Conditional Probabilities: ÚFAL Submission to the SIGTYP 2020 Shared Task,Martin Vastl|Daniel Zeman|Rudolf Rosa,,38939792,https://www.aclweb.org/anthology/2020.sigtyp-1.4 +WS-11,WS-11.8,SNACS Annotation of Case Markers and Adpositions in Hindi,Aryaman Arora|Nathan Schneider,,38939800, +WS-11,WS-11.816,Cross-lingual Alignment Methods for Multilingual BERT: A Comparative Study,Saurabh Kulshreshtha|‪José Luis Redondo García|Ching-Yun Chang,,38940629,https://www.aclweb.org/anthology/2020.findings-emnlp.83 +WS-11,WS-11.9,Multilingual Jointly Trained Acoustic and Written Word Embeddings,Yushi Hu|Shane Settle|Karen Livescu,,38939801, +WS-12,WS-12.1,Dilated Convolutional Attention Network for Medical Code Assignment from Clinical Text,Shaoxiong Ji|Erik Cambria|Pekka Marttinen,,38939807,https://www.aclweb.org/anthology/2020.clinicalnlp-1.8 +WS-12,WS-12.10,Information Extraction from Swedish Medical Prescriptions with Sig-Transformer Encoder,John Pougué Biyong|Bo Wang|Terry Lyons|Alejo Nevado-Holgado,,38939813,https://www.aclweb.org/anthology/2020.clinicalnlp-1.5 +WS-12,WS-12.1041,Learning to Generate Clinically Coherent Chest X-Ray Reports,Justin Lovelace|Bobak Mortazavi,,38940177,https://www.aclweb.org/anthology/2020.findings-emnlp.110 +WS-12,WS-12.11,How You Ask Matters: The Effect of Paraphrastic Questions to BERT Performance on a Clinical SQuAD Dataset,Sungrim,,38939814,https://www.aclweb.org/anthology/2020.clinicalnlp-1.13 +WS-12,WS-12.12,Multiple Sclerosis Severity Classification From Clinical Text,Alister D'Costa|Stefan Denkovski|Michal Malyska|Sae Young Moon|Brandon Rufino|Zhen Yang|Taylor Killian|Marzyeh Ghassemi,,38939815,https://www.aclweb.org/anthology/2020.clinicalnlp-1.2 +WS-12,WS-12.13,An Ensemble Approach to Automatic Structuring of Radiology Reports,Morteza Pourreza Shahri|Amir Tahmasebi|Bingyang Ye|Henghui Zhu|Javed Aslam|Timothy Ferris,,38939816,https://www.aclweb.org/anthology/2020.clinicalnlp-1.28 +WS-12,WS-12.15,Various Levels of Representation for Predicting Stroke Prognosis using Text Records of Magnetic Resonance Imaging,Tak-Sung Heo|Chulho Kim|Jeong-Myeong Choi|Yeong-Seok Jeong|Yu-Seop Kim,,38939817,https://www.aclweb.org/anthology/2020.clinicalnlp-1.1 +WS-12,WS-12.16,Relative and Incomplete Time Expression Anchoring for Clinical Text,Louise Dupuis|Nicol Bergou|Hegler Tissot|Sumithra Velupillai,,38939818,https://www.aclweb.org/anthology/2020.clinicalnlp-1.14 +WS-12,WS-12.1640,Learning Visual-Semantic Embeddings for Reporting Abnormal Findings on Chest X-rays,Jianmo Ni|Chun-Nan Hsu|Amilcare Gentili|Julian McAuley,,38940178,https://www.aclweb.org/anthology/2020.findings-emnlp.176 +WS-12,WS-12.17,MeDAL: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining,Zhi Wen|Xing Han Lu|Siva Reddy,,38939819,https://www.aclweb.org/anthology/2020.clinicalnlp-1.15 +WS-12,WS-12.1713,Characterizing the Value of Information in Medical Notes,Chao-Chun Hsu|Shantanu Karnwal|Sendhil Mullainathan|Ziad Obermeyer|Chenhao Tan,,38940179,https://www.aclweb.org/anthology/2020.findings-emnlp.187 +WS-12,WS-12.18,Evaluation of Transfer Learning for Adverse Drug Event (ADE) and Medication Entity Extraction,Sankaran Narayanan|Kaivalya Mannam|Sreeranga P Rajan|P Venkat Rangan,,38939820,https://www.aclweb.org/anthology/2020.clinicalnlp-1.6 WS-12,WS-12.19,"Knowledge Grounded Conversational Symptom Detection - with Graph Memory Networks",Hongyin Luo|Shang-Wen Li|James Glass,38939821 -WS-12,WS-12.20,Pretrained Language Models for Biomedical and Clinical Tasks: Understanding and Extending the State-of-the-Art,Patrick Lewis|Myle Ott|Jingfei Du|Veselin Stoyanov,38939822 -WS-12,WS-12.21,Incorporating Risk Factor Embeddings in Pre-trained Transformers Improves Sentiment Prediction in Psychiatric Discharge Summaries,Xiyu Ding|Mei-Hua Hall|Timothy Miller,38939823 -WS-12,WS-12.2127,PharmMT: A Neural Machine Translation Approach to Simplify Prescription Directions,Jiazhao Li|Corey Lester|Xinyan Zhao|Yuting Ding|Yun Jiang|V.G.Vinod Vydiswaran,38940180 -WS-12,WS-12.23,Assessment of DistilBERT performance on Named Entity Recognition task for the detection of Protected Health Information and medical concepts,Macarious Abadeer,38939824 -WS-12,WS-12.25,Distinguishing between Dementia with Lewy bodies (DLB) and Alzheimer's Disease (AD) using Mental Health Records: a Classification Approach,Zixu Wang|Julia Ive|Sinead Moylett|Christoph Mueller|Rudolf Cardinal|Sumithra Velupillai|John O'Brien|Robert Stewart,38939825 -WS-12,WS-12.2509,A Dual-Attention Network for Joint Named Entity Recognition and Sentence Classification of Adverse Drug Events,Susmitha Wunnava|Xiao Qin|Tabassum Kakar|Xiangnan Kong|Elke A. Rundensteiner,38940181 -WS-12,WS-12.26,Weakly Supervised Medication Regimen Extraction from Medical Conversations,Dhruvesh Patel|Sandeep Konam|Sai Prabhakar,38939826 -WS-12,WS-12.27,Extracting Relations between Radiotherapy Treatment Details,Danielle Bitterman|Timothy Miller|David Harris|Chen Lin|Sean Finan|Jeremy Warner|Raymond Mak|Guergana Savova,38939827 -WS-12,WS-12.2801,Dr. Summarize: Global Summarization of Medical Dialogue by Exploiting Local Structures,Anirudh Joshi|Namit Katariya|Xavier Amatriain|Anitha Kannan,38940182 -WS-12,WS-12.2804,Generating Accurate Electronic Health Assessment from Medical Graph,Zhichao Yang|Hong Yu,38940183 -WS-12,WS-12.29,The Chilean Waiting List Corpus: a new resource for clinical Named Entity Recognition in Spanish,Pablo Báez|Fabián Villena|Matías Rojas|Manuel Durán|Jocelyn Dunstan,38939828 -WS-12,WS-12.3,Classification of Syncope Cases in Norwegian Medical Records,Ildiko Pilan|Pål H. Brekke|Fredrik A. Dahl|Tore Gundersen|Haldor Husby|Øystein Nytrø|Lilja Øvrelid,38939808 -WS-12,WS-12.30,BioBERTpt - A Portuguese Neural Language Model for Clinical Named Entity Recognition,Elisa Terumi Rubel Schneider|João Vitor Andrioli De Souza|Julien Knafou|Lucas Emanuel Silva E Oliveira|Jenny Copara|Yohan Bonescki Gumiel|Lucas Ferro Antunes De Oliveira|Emerson Cabrera Paraiso|Douglas Teodoro|Cláudia Maria Cabral Moro Barra,38939829 -WS-12,WS-12.31,Cancer Registry Information Extraction via Transfer Learning,Yan-Jie Lin|Hong-Jie Dai|You-Chen Zhang|Chung-Yang Wu|Yu-Cheng Chang|Pin-Jou Lu|Chih-Jen Huang|Yu-Tsang Wang|Hui-Min Hsieh|Kun-San Chao|Tsang-Wu Liu|I-Shou Chang|Yi-Hsin Connie Yang|Ti-Hao Wang|Ko-Jiunn Liu|Li-Tzong Chen|Sheau-Fang Yang,38939830 -WS-12,WS-12.33,Utilizing Multimodal Feature Consistency to Detect Adversarial Examples on Clinical Summaries,Wenjie Wang|Youngja Park|Taesung Lee|Ian Molloy|Pengfei Tang|Li Xiong,38939831 -WS-12,WS-12.37,PHICON: Improving Generalization of Clinical Text De-identification Models via Data Augmentation,Xiang Yue|Shuang Zhou,38939832 -WS-12,WS-12.38,Where's the Question? A Multi-channel Deep Convolutional Neural Network for Question Identification in Textual Data,George Michalopoulos|Helen Chen|Alexander Wong,38939833 -WS-12,WS-12.39,Advancing Seq2seq with Joint Paraphrase Learning,So Yeon Min|Preethi Raghavan|Peter Szolovits,38939834 -WS-12,WS-12.4,Comparison of Machine Learning Methods for Multi-label Classificationof Nursing Education and Licensure Exam Questions,John Langton|Krishna Srihasam|Junlin Jiang,38939809 -WS-12,WS-12.40,Learning from Unlabelled Data for Clinical Semantic Textual Similarity,Yuxia Wang|Karin Verspoor|Timothy Baldwin,38939835 -WS-12,WS-12.43,BERT-XML: Large Scale Automated ICD Coding Using BERTPretraining,Zachariah Zhang|Jingshu Liu|Narges Razavian,38939836 -WS-12,WS-12.44,Joint Learning with Pre-trained Transformer on Named Entity Recognition and Relation Extraction Tasks for Clinical Analytics,Miao Chen|Ganhui Lan|Fang Du|Victor Lobanov,38939837 -WS-12,WS-12.48,Analyzing Text Specific vs Blackbox Fairness Algorithms in Multimodal Clinical NLP,John Chen|Ian Berlot-Attwell|Xindi Wang|Safwan Hossain|Frank Rudzicz,38939838 -WS-12,WS-12.49,Extracting Semantic Aspects for Structured Representation of Clinical Trial Eligibility Criteria,Tirthankar Dasgupta|Ishani Mondal|Abir Naskar|Lipika Dey,38939839 -WS-12,WS-12.6,Clinical XLNet: Modeling Sequential Clinical Notes and Predicting Prolonged Mechanical Ventilation,Kexin Huang|Abhishek Singh|Sitong Chen|Edward Moseley|Chih-Ying Deng|Naomi George|Charolotta Lindvall,38939810 -WS-12,WS-12.7,Automatic recognition of abdominal lymph nodes from clinical text,Yifan Peng|Sungwon Lee|Daniel C. Elton|Thomas Shen|Yu-xing Tang|Qingyu Chen|Shuai Wang|Yingying Zhu|Ronald Summers|Zhiyong Lu,38939811 -WS-12,WS-12.8,On the diminishing return of labeling clinical reports,Jean-Baptiste Lamare|Oloruntobiloba Olatunji|Li Yao,38939812 -WS-12,WS-12.KEY,Keynote 3rd Clinical Natural Language Processing Workshop,Hong Yu,38939840 -WS-13,WS-13.1,Correcting the Misuse: A Method for the Chinese Idiom Cloze Test,Xinyu Wang|Hongsheng Zhao|Tan Yang|Hongbo Wang,38939724 -WS-13,WS-13.1008,AutoETER: Automated Entity Type Representation for Knowledge Graph Embedding,Yongfei Zhang,38940167 -WS-13,WS-13.1039,Dynamic Semantic Matching and Aggregation Network for Few-shot Intent Detection,Hoang Nguyen,38940168 -WS-13,WS-13.1059,Biomedical Event Extraction on Graph Edge-conditioned Attention Networks with Hierarchical Knowledge Graphs,Kung-Hsiang Huang,38940169 -WS-13,WS-13.12,Entity Attribute Relation Extraction with Attribute-Aware Embeddings,Dan Iter|Xiao Yu|Fangtao Li,38939729 -WS-13,WS-13.1524,Continual Learning Long Short Term Memory,Xiaolong Wang,38940170 -WS-13,WS-13.16,Enhancing Question Answering by Injecting Ontological Knowledgethrough Regularization,Travis Goodwin|Dina Demner-Fushman,38939730 -WS-13,WS-13.17,Target Concept Guided Medical Concept Normalization in Noisy User-Generated Texts,Katikapalli Subramanyam Kalyan|Sivanesan Sangeetha,38939731 -WS-13,WS-13.18,Incorporating Commonsense Knowledge Graph in Pretrained Models for Social Commonsense Tasks,Ting-Yun Chang|Yang Liu|Karthik Gopalakrishnan|Behnam Hedayatnia|Pei Zhou|Dilek Hakkani-Tur,38939732 -WS-13,WS-13.20,Commonsense Statements Identification and Explanation with Transformer based Encoders,Sonia Cibu|Anca Marginean,38939733 -WS-13,WS-13.2047,Out-of-Sample Representation Learning for Knowledge Graphs,Marjan Albooyeh,38940171 -WS-13,WS-13.2085,Visual Objects As Context: Exploiting Visual Objects for Lexical Entailment,Masayasu Muraoka,38940172 -WS-13,WS-13.22,"On the Complementary Nature of Knowledge Graph Embedding, Fine Grain Entity Types, and Language Modeling",Rajat Patel|Francis Ferraro,38939734 -WS-13,WS-13.2513,BERT-kNN: Adding a kNN Search Component to Pretrained Language Models for Better QA,Nora Kassner,38940173 -WS-13,WS-13.2666,Cross-Lingual Text Classification with Minimal Resources by Transferring a Sparse Teacher,Giannis Karamanolakis,38940174 -WS-13,WS-13.4,Relation Extraction with Contextualized Relation Embedding,Xiaoyu Chen|Rohan Badlani,38939725 -WS-13,WS-13.6,Generalization to Mitigate Synonym Substitution Attacks,Basemah Alshemali|Jugal Kalita,38939726 -WS-13,WS-13.696,E-BERT: Efficient-Yet-Effective Entity Embeddings for BERT,Nina Poerner,38940166 -WS-13,WS-13.7,Data Augmentation for Finetuning Text Generators,Steven Y. Feng|Varun Gangal|Dongyeop Kang|Teruko Mitamura|Eduard Hovy,38939727 -WS-13,WS-13.9,Common Sense or World Knowledge? Investigating Adapter-Based Knowledge Injection into Pretrained Transformers,Anne Lauscher|Olga Majewska|Leonardo F. R. Ribeiro|Iryna Gurevych|Nikolai Rozanov|Goran Glavaš,38939728 -WS-13,WS-13.WS13_Keynote1,Keynote talk by Eduard Hovy,Eduard Hovy,38940164 -WS-13,WS-13.WS13_Keynote2,You can lead a horse to water...: Representing vs. Using Features in Neural NLP,Ellie Pavlick,38940165 -WS-15,WS-15.1,Knowing Right from Wrong: Should We Use More Complex Models for Automatic Short-Answer Scoring in Bahasa Indonesia?,Ali Akbar Septiandri|Yosef Ardhito Winatmoko|Ilham Firdausi Putra,38939419 -WS-11,WS-11.1093,"Parsing with Multilingual BERT, a Small Corpus, and a Small Treebank",Ethan C. Chau,38940125 -WS-15,WS-15.1098,OptSLA: an Optimization-Based Approach for Sequential Label Aggregation,Qi Li,38940107 -WS-15,WS-15.12,Exploring the Boundaries of Low-Resource BERT Distillation,Moshe Wasserblat|Oren Pereg|Peter Izsak,38939426 -WS-15,WS-15.1286,Inexpensive Domain Adaptation of Pretrained Language Models: Case Studies on Biomedical NER and Covid-19 QA,Nina Poerner,38940121 -WS-15,WS-15.13,Efficient Estimation of Influence of a Training Instance,Sosuke Kobayashi|Sho Yokoi|Jun Suzuki|Kentaro Inui,38939427 -WS-15,WS-15.14,Efficient Inference For Neural Machine Translation,Yi-Te Hsu|Sarthak Garg|Yi-Hsiu Liao|Ilya Chatsviorkin,38939429 -WS-15,WS-15.1418,TopicBERT for Energy Efficient Document Classification,Yatin Chaudhary,38940122 -WS-15,WS-15.1537,Constrained Decoding for Computationally Efficient Named Entity Recognition Taggers,Brian Lester,38940105 -WS-15,WS-15.17,Sparse Optimization for Unsupervised Extractive Summarization of Long Documents with the Frank-Wolfe Algorithm,Alicia Tsai|Laurent El Ghaoui,38939430 -WS-4,WS-4.1761,ConveRT: Efficient and Accurate Conversational Representations from Transformers,Ivan Vuliƒá,38940128 -WS-15,WS-15.1887,Semi-supervised Formality Style Transfer using LanguageModel Discriminator and Mutual Information Maximization,Kunal Chawla,38940140 -WS-15,WS-15.2,Rank and run-time aware compression of NLP Applications,Urmish Thakker|Jesse Beu|Dibakar Gope|Ganesh Dasika|Matthew Mattina,38939420 -WS-15,WS-15.2015,Blockwise Self-Attention for Long Document Understanding,Jiezhong Qiu,38940119 -WS-15,WS-15.2017,Unsupervised Few-Bits Semantic Hashing with Implicit Topics Modeling,Fanghua Ye,38940106 -WS-15,WS-15.2182,DiPair: Fast and Accurate Distillation for Trillion-ScaleText Matching and Pair Modeling,Jiecao Chen,38940104 -WS-15,WS-15.22,Don't Read Too Much Into It: Adaptive Computation for Open-Domain Question Answering,Yuxiang Wu|Pasquale Minervini|Pontus Stenetorp|Sebastian Riedel,38939431 -WS-17,WS-17.2217,A little goes a long way: Improving toxic language classification despite data scarcity,Tommi Gröndahl,38940137 -WS-15,WS-15.2230,General Purpose Text Embeddings from Pre-trained Language Models for Scalable Inference,Myle Ott,38940109 -WS-15,WS-15.2288,Domain Adversarial Fine-Tuning as an Effective Regularizer,Giorgos Vernikos,38940129 -WS-15,WS-15.2453,Improve Transformer Models with Better Relative Position Embeddings,Zhiheng Huang,38940108 -WS-16,WS-16.2487,Composed Variational Natural Language Generation for Few-shot Intents,Congying Xia,38940130 -WS-15,WS-15.2516,Identifying Spurious Correlations for Robust Text Classification,Zhao Wang,38940117 -WS-3,WS-3.2575,What do we expect from Multiple-choice QA Systems?,Krunal Shah,38940132 -WS-15,WS-15.27,Doped Structured Matrices for Extreme Compression of LSTM Models,Urmish Thakker,38940744 -WS-13,WS-13.2797,Adversarial Augmentation Policy Search for Domain and Cross-Lingual Generalization in Reading Comprehension,Adyasha Maharana,38940111 -WS-15,WS-15.28,A Two-stage Model for Slot Filling in Low-resource Settings: Domain-agnostic Non-slot Reduction and Pretrained Contextual Embeddings,Cennet Oguz|Ngoc Thang Vu,38939432 -WS-13,WS-13.2800,Denoising Multi-Source Weak Supervision for Neural Text Classification,Chao Zhang,38940139 -WS-15,WS-15.29,Early Exiting BERT for Efficient Document Ranking,Ji Xin|Rodrigo Nogueira|Yaoliang Yu|Jimmy Lin,38939433 -WS-25,WS-25.2976,NLP Service APIs and Models for Efficient Registration of New Clients,Sahil Shah,38940136 -WS-15,WS-15.3,Incremental Neural Coreference Resolution in Constant Memory,Patrick Xia|João Sedoc|Benjamin Van Durme,38939421 -WS-15,WS-15.30,Keyphrase Generation with GANs in Low-Resources Scenarios,Giuseppe Lancioni|Saida S.Mohamed|Beatrice Portelli|Giuseppe Serra|Carlo Tasso,38939434 -WS-15,WS-15.3078,SupMMD: A Sentence Importance Model for Extractive Summarization using Maximum Mean Discrepancy,Umanga Bista,38940131 -WS-15,WS-15.32,Quasi-Multitask Learning: an Efficient Surrogate for Obtaining Model Ensembles,Norbert Kis-Szabó|Gábor Berend,38939435 -WS-15,WS-15.34,A Little Bit Is Worse Than None: Ranking with Limited Training Data,Xinyu Zhang|Andrew Yates|Jimmy Lin,38939436 -WS-15,WS-15.3444,Multi-hop Question Generation with Graph Convolutional Network,Dan Su,38940120 -WS-15,WS-15.3459,Guiding Attention for Self-Supervised Learning with Transformers,Ameet Deshpande,38940124 -WS-6,WS-6.3476,Compressing Transformer-Based Semantic Parsing Models using Compositional Code Embeddings,Prafull Prakash,38940116 -WS-15,WS-15.3526,Probabilstic Case-based Reasoning for Open-World Knowledge Graph Completion,Rajarshi Das,38940133 -WS-25,WS-25.3561,On the Sub-Layer Functionalities of Transformer Decoder,Yilin Yang,38940141 -WS-15,WS-15.3562,Extremely Low Bit Transformer Quantization for On-Device Neural Machine Translation,Insoo Chung,38940118 -WS-15,WS-15.36,Predictive Model Selection for Transfer Learning in Sequence Labeling Tasks,Parul Awasthy|Bishwaranjan Bhattacharjee|John Kender|Radu Florian,38939437 -WS-15,WS-15.361,Understanding tables with intermediate pre-training,Julian Martin Eisenschlos,38940134 -WS-15,WS-15.37,Load What You Need: Smaller Versions of Mutlilingual BERT,Amine Abdaoui|Camille Pradel|Grégoire Sigel,38939438 -WS-15,WS-15.38,SqueezeBERT: What can computer vision teach NLP about efficient neural networks?,Forrest Iandola|Albert Shaw|Ravi Krishna|Kurt Keutzer,38939439 -WS-15,WS-15.381,Enhance Robustness of Sequence Labelling with Masked Adversarial Training,Luoxin Chen,38940127 -WS-15,WS-15.39,Analysis of Resource-efficient Predictive Models for Natural Language Processing,Raj Pranesh|Ambesh Shekhar,38939440 -WS-15,WS-15.42,Towards Accurate and Reliable Energy Measurement of NLP Models,Qingqing Cao|Aruna Balasubramanian|Niranjan Balasubramanian,38939441 -WS-15,WS-15.43,FastFormers: Highly Efficient Transformer Models for Natural Language Understanding,Young Jin Kim|Hany Hassan,38939442 -WS-15,WS-15.45,A comparison between CNNs and WFAs for Sequence Classification,Ariadna Quattoni|Xavier Carreras,38939443 -WS-15,WS-15.46,Counterfactual Augmentation for Training Next Response Selection,Seungtaek Choi|Myeongho Jeong|Jinyoung Yeo|Seung-won Hwang,38939444 -WS-15,WS-15.47,Do We Need to Create Big Datasets to Learn a Task?,Swaroop Mishra|Bhavdeep Singh Sachdeva,38939445 -WS-15,WS-15.49,Guiding Attention for Self-Supervised Learning with Transformers,Ameet Deshpande|Karthik Narasimhan,38939446 -WS-21,WS-21.490,A Fully Hyperbolic Neural Model for Hierarchical Multi-Class Classification,Federico López,38940126 -WS-15,WS-15.5,Learning Informative Representations of Biomedical Relations with Latent Variable Models,Harshil Shah|Julien Fauqueur,38939422 -WS-1,WS-1.510,Dynamic Data Selection for Curriculum Learning via Ability Estimation,John P. Lalor,38940123 -WS-15,WS-15.512,Fixed Encoder Self-Attention Patterns in Transformer-Based Machine Translation,Alessandro Raganato,38940110 -WS-15,WS-15.547,PBoS: Probabilistic Bag-of-Subwords for Generalizing Word Embedding,Zhao Jinman,38940115 -WS-15,WS-15.6,End to End Binarized Neural Networks for Text Classification,Kumar Shridhar|Harshil Jain|Akshat Agarwal|Denis Kleyko,38939423 -WS-15,WS-15.651,Pruning Redundant Mappings in Transformer Models via Spectral-Normalized Identity Prior,Zi Lin,38940112 -WS-15,WS-15.724,Improving QA Generalization by Concurrent Modeling of Multiple Biases,Mingzhu Wu,38940113 -WS-15,WS-15.8,Large Product Key Memory for Pre-trained Language Models,Gyuwan Kim|Tae Hwan Jung,38939424 -WS-13,WS-13.884,Generative Data Augmentation for Commonsense Reasoning,Yiben Yang,38940138 -WS-15,WS-15.9,P-SIF: Document Embeddings using Partition Averaging,Vivek Gupta|Ankit Saw|Pegah Nokhiz|Praneeth Netrapalli|Piyush Rai|Partha Talukdar,38939425 -WS-15,WS-15.929,ESTeR: Combining Word Co-occurrences and Word Associations for Unsupervised Emotion Detection,Polina Rozenshtein,38940135 -WS-16,WS-16.10,Exploring Span Representations in Neural Coreference Resolution,Patrick Kahardipraja|Olena Vyshnevska|Sharid Loáiciga,38939689 -WS-16,WS-16.11,Supporting Comedy Writers: Predicting Audience's Response from Sketch Comedy and Crosstalk Scripts,Maolin Li,38939690 -WS-16,WS-16.13,Exploring Coreference Features in Heterogeneous Data with Text Classification,Ekaterina Lapshinova-Koltunski|Kerstin Kunz,38939691 -WS-16,WS-16.14,Contextualized Embeddings for Connective Disambiguation in Shallow Discourse Parsing,René Knaebel|Manfred Stede,38939692 -WS-16,WS-16.15,DSNDM: Deep Siamese Neural Discourse Model with Attention for Text Pairs Categorization and Ranking,Alexander Chernyavskiy|Dmitry Ilvovsky,38939693 -WS-16,WS-16.17,Do sentence embeddings capture discourse properties of sentences from Scientific Abstracts ?,Laurine Huber|Chaker Memmadi|Mathilde Dargnat|Yannick Toussaint,38939694 -WS-16,WS-16.18,Unsupervised Inference of Data-Driven Discourse Structures using a Tree Auto-Encoder,Patrick Huber|Giuseppe Carenini,38939695 -WS-16,WS-16.19,Large Discourse Treebanks from Scalable Distant Supervision,Patrick Huber|Giuseppe Carenini,38939696 -WS-16,WS-16.1951,Adapting Coreference Resolution to Twitter Conversations,Berfin Aktaş,38940697 -WS-16,WS-16.20,"Discourse for Argument Mining, and Argument Mining as Discourse",Diane Litman,38939697 -WS-16,WS-16.208,Converting the Point of View of Messages Spoken to Virtual Assistants,Isabelle G. Lee,38940694 -WS-16,WS-16.21,Joint Modeling of Arguments for Event Understanding,Yunmo Chen|Tongfei Chen|Benjamin Van Durme,38939698 -WS-16,WS-16.2195,Social Commonsense Reasoning with Multi-Head Knowledge Attention,Debjit Paul,38940698 -WS-16,WS-16.22,Analyzing Neural Discourse Coherence Models,Youmna Farag|Josef Valvoda|Helen Yannakoudakis|Ted Briscoe,38939699 -WS-16,WS-16.23,Computational Interpretation of Recency for the Choice of Referring Expressions in Discourse,Fahime Same|Kees Van Deemter,38939700 -WS-16,WS-16.24,Do We Really Need That Many Parameters In Transformer For Extractive Summarization? Discourse Can Help !,Wen Xiao|Patrick Huber|Giuseppe Carenini,38939701 -WS-16,WS-16.2487-WS16,Composed Variational Natural Language Generation for Few-shot Intents,Congying Xia,38940699 -WS-16,WS-16.26,Extending Implicit Discourse Relation Recognition to the PDTB-3,Li Liang|Zheng Zhao|Bonnie Webber,38939702 -WS-16,WS-16.263,A Hierarchical Network for Abstractive Meeting Summarization with Cross-Domain Pretraining,Chenguang Zhu,38940695 -WS-16,WS-16.27,"TED-MDB Lexicons: TrEnConnLex, PtEnConnLex",Murathan Kurfalı|Sibel Ozer|Deniz Zeyrek|Amália Mendes,38939703 -WS-16,WS-16.28,Eliminating Mention Overlaps: Evaluation of Coreference Resolution Systems Under Adversarial Attacks,Haixia Chai|Wei Zhao|Steffen Eger|Michael Strube,38939704 -WS-16,WS-16.29,Exploring aspects of similarity between spoken personal narratives by disentangling them into narrative clause types,Belen Saldias|Deb Roy,38939705 -WS-16,WS-16.31,Coreference for Discourse Parsing: A Neural Approach,Grigorii Guz|Giuseppe Carenini,38939706 -WS-16,WS-16.3452,Thinking Like a Skeptic: Defeasible Inference in Natural Language,Rachel Rudinger,38940700 -WS-16,WS-16.3598,Paraphrasing vs Coreferring: Two Sides of the Same Coin,Yehudit Meged,38940701 -WS-16,WS-16.4,How does discourse affect Spanish-Chinese Translation? A case study based on a Spanish-Chinese parallel corpus,Shuyuan Cao,38939686 -WS-16,WS-16.475,Dialogue Generation on Infrequent Sentence Functions via Structured Meta-Learning,Yifan Gao,38940696 -WS-16,WS-16.6,Free the Plural: Unrestricted Split-Antecedent Anaphora Resolution,Juntao Yu|Nafise Sadat Moosavi|Silviu Paun|Massimo Poesio,38940702 -WS-16,WS-16.7,Multitask Learning-Based Neural Bridging Reference Resolution,Juntao Yu|Massimo Poesio,38940703 -WS-16,WS-16.8,Beyond Adjacency Pairs: Extracting Longer Regularities in Human-Machine Dialogues,Maitreyee Maitreyee,38939687 -WS-16,WS-16.9,Using Type Information to Improve Entity Coreference Resolution,Sopan Khosla|Carolyn Rose,38939688 -WS-17,WS-17.10,A Novel Methodology for Developing Automatic Harassment Classifiers for Twitter,Ishaan Arora|Julia Guo|Sarah Ita Levitan|Susan McGregor|Julia Hirschberg,38939517 -WS-17,WS-17.13,Countering hate on social media: Large scale classification of hate and counter speech,Joshua Garland|Keyan Ghazi-Zahedi|Jean-Gabriel Young|Laurent Hébert-Dufresne|Mirta Galesic,38939518 -WS-17,WS-17.15,Impact of politically biased data on hate speech classification,Maximilian Wich|Jan Bauer|Georg Groh,38939519 -WS-17,WS-17.16,A Unified Taxonomy of Harmful Content,Michele Banko|Brendon MacKeen|Laurie Ray,38939520 -WS-17,WS-17.19,In Data We Trust: A Critical Analysis of Hate Speech Detection Datasets,Kosisochukwu Madukwe|Xiaoying Gao|Bing Xue,38939521 -WS-17,WS-17.2,Moderating Our (Dis)Content: Renewing the Regulatory Approach,Claire Pershan,38939516 -WS-17,WS-17.24,Fine-tuning BERT for multi-domain and multi-label incivil language detection,Kadir Bulut Ozler|Kate Kenski|Steve Rains|Yotam Shmargad|Kevin Coe|Steven Bethard,38939522 -WS-17,WS-17.25,Six Attributes of Unhealthy Conversations,Ilan Price|Jordan Gifford-Moore|Jory Flemming|Saul Musker|Maayan Roichman|Guillaume Sylvain|Nithum Thain|Lucas Dixon|Jeffrey Sorensen,38939523 -WS-17,WS-17.31,Reducing Unintended Identity Bias in Russian Hate Speech Detection,Nadezhda Zueva|Madina Kabirova|Pavel Kalaidin,38939524 -WS-17,WS-17.32,Investigating takedowns of abuse on Twitter,Rosalie Gillett|Nicolas Suzor|Jean Burgess|Bridget Harris|Molly Dragiewicz,38939525 -WS-17,WS-17.37,Detecting East Asian Prejudice on Social Media,Bertie Vidgen|Scott Hale|Ella Guest|Helen Margetts|David Broniatowski|Zeerak Waseem|Austin Botelho|Matthew Hall|Rebekah Tromble,38939526 -WS-17,WS-17.39,Investigating Sampling Bias in Abusive Language Detection,Dante Razo|Sandra Kübler,38939527 -WS-17,WS-17.42,Is your toxicity my toxicity? Understanding the influence of rater identity on perceptions of toxicity,Ian Kivlichan|Olivia Redfield|Rachel Rosen|Raquel Saxe|Nitesh Goyal|Lucy Vasserman,38939528 -WS-17,WS-17.43,Free Expression by Design: Improving in-platform mechanisms and third-party tools to tackle online abuse,Viktorya Vilk|Elodie Vialle|Matt Bailey,38939529 -WS-17,WS-17.44,HurtBERT: Incorporating Lexical Features with BERT for the Detection of Abusive Language,Anna Koufakou|Endang Wahyu Pamungkas|Valerio Basile|Viviana Patti,38939530 -WS-17,WS-17.47,Developing a New Classifier for Automated Identification of Incivility in Social Media,Sam Davidson|Qiusi Sun|Magdalena Wojcieszak,38939531 -WS-17,WS-17.48,Abusive Language Detection using Syntactic Dependency Graphs,Kanika Narang|Chris Brew,38939532 -WS-17,WS-17.49,Towards a Comprehensive Taxonomy and Large-Scale Annotated Corpus for Online Slur Usage,Jana Kurrek|Haji Mohammad Saleem|Derek Ruths,38939533 -WS-17,WS-17.50,Attending the Emotions to Detect Online Abusive Language,Niloofar Safi Samghabadi|Afsheen Hatami|Mahsa Shafaei|Sudipta Kar|Thamar Solorio,38939534 -WS-17,WS-17.51,Enhancing the Identification of Cyberbullying through Participant Roles,Gathika Rathnayake|Thushari Atapattu|Mahen Herath|Georgia Zhang|Katrina Falkner,38939535 -WS-17,WS-17.52,Using Transfer-based Language Models to Detect Hateful and Offensive Language Online,Vebjørn Isaksen|Björn Gambäck,38939536 -WS-17,WS-17.56,On Cross-Dataset Generalization in Automatic Detection of Online Abuse,Isar Nejadgholi|Svetlana Kiritchenko,38939537 -WS-17,WS-17.57,Identifying and Measuring Annotator Bias Based on Annotators' Demographic Characteristics,Hala Al Kuwatly|Maximilian Wich|Georg Groh,38939538 -WS-17,WS-17.58,Investigating Annotator Bias with a Graph-Based Approach,Maximilian Wich|Hala Al Kuwatly|Georg Groh,38939539 -WS-17,WS-17.818,Findings: Hybrid Emoji-Based Masked Language Models for Zero-Shot Abusive Language Detection,Michele Corazza|Stefano Menini|Elena Cabrio|Sara Tonelli|Serena Villata,38941226 -WS-17,WS-17.K1,Keynote1,André Brock,38939540 -WS-17,WS-17.K2,Keynote2,Alex Hanna & Maliha Ahmed,38939541 -WS-17,WS-17.K3,Keynote3,Maria Y. Rodriguez,38939542 -WS-18,WS-18.13,Measuring Linguistic Diversity During COVID-19,Jonathan Dunn|Tom Coupe|Benjamin Adams,38940618 -WS-18,WS-18.15,How Language Influences Attitudes Toward Brands,David DeFranza|Arul Mishra|Himanshu Mishra,38940628 -WS-18,WS-18.17,Using BERT for Qualitative Content Analysis in Psychosocial Online Counseling,Philipp Grandeit|Carolyn Haberkern|Maximiliane Lang|Jens Albrecht|Robert Lehmann,38940609 -WS-18,WS-18.19,Swimming with the Tide? Positional Claim Detection across Political Text Types,Nico Blokker|Erenay Dayanik|Gabriella Lapesa|Sebastian Padó,38940616 -WS-18,WS-18.20,Does Social Support (Expressed in Post Titles) Elicit Comments in Online Substance Use Recovery Forums?,Anietie|Y|Sharath Chandra Guntuku,38940623 -WS-18,WS-18.21,I miss you babe: Analyzing Emotion Dynamics During COVID-19 Pandemic,Hui Xian Lynnette Ng|Roy Ka-Wei Lee|Md Rabiul Awal,38940603 -WS-18,WS-18.22,"Assessing population-level symptoms of anxiety, depression, and suicide risk in real time using NLP applied to social media data",Alex Fine|Patrick Crutchley|Jenny Blase|Joshua Carroll|Glen Coppersmith,38940624 -WS-18,WS-18.23,Topic preference detection: A novel approach to understand perspective taking in conversation,Michael Yeomans|Alison Wood Brooks,38940626 -WS-18,WS-18.26,Viable Threat on News Reading: Generating Biased News Using Natural Language Models,Saurabh Gupta|Hong Huy Nguyen|Junichi Yamagishi|Isao Echizen,38940610 -WS-18,WS-18.28,A Lexical Semantic Leadership Network of Nineteenth CenturyAbolitionist Newspapers,Sandeep Soni|Lauren Klein|Jacob Eisenstein,38940625 -WS-18,WS-18.29,Unsupervised Anomaly Detection in Parole Hearings using Language Models,Authors: Graham Todd|Catalin Voss|Jenny Hong,38940611 -WS-18,WS-18.32,Identifying Worry in Twitter: Beyond Emotion Analysis,Reyha Verma|Christian Von Der Weth|Jithin Vachery|Mohan Kankanhalli,38940602 -WS-18,WS-18.33,"Text Zoning and Classification for Job Advertisements in German, French and English",Ann-Sophie Gnehm|Simon Clematide,38940604 -WS-18,WS-18.34,Is Wikipedia succeeding in reducing gender bias? Assessing changes in gender bias in Wikipedia using word embeddings,Katja Geertruida Schmahl|Tom Julian Viering|Stavros Makrodimitris|Arman Naseri Jahfari|David Tax|Marco Loog,38940605 -WS-18,WS-18.36,Effects of Anonymity on Comment Persuasiveness in Wikipedia Articles for Deletion Discussions,Yimin Xiao|Lu Xiao,38940619 -WS-18,WS-18.37,"Uncertainty over Uncertainty: Investigating the Assumptions, Annotations, and Text Measurements of Economic Policy Uncertainty",Katherine Keith|Christoph Teichmann|Brendan O'Connor|Edgar Meij,38940620 -WS-18,WS-18.38,Recalibrating classifiers for interpretable abusive content detection,Bertie Vidgen|Scott Hale|Sam Staton|Tom Melham|Helen Margetts|Ohad Kammar|Marcin Szymczak,38940621 -WS-18,WS-18.43,Predicting independent living outcomes from written reports of social workers,Angelika Maier|Philipp Cimiano,38940617 -WS-18,WS-18.44,Analyzing Political Bias and Unfairness in News Articles at Different Levels of Granularity,Wei-Fan Chen|Khalid Al Khatib|Henning Wachsmuth|Benno Stein,38940612 -WS-18,WS-18.45,Mapping Local News Coverage: Precise location extraction in textual news content using fine-tuned BERT based language model,Sarang Gupta|Kumari Nishu,38940613 -WS-18,WS-18.47,Foreigner-directed speech is simpler than native-directed: Evidence from social media,Aleksandrs Berdicevskis,38940614 -WS-18,WS-18.49,Diachronic Embeddings for People in the News,Felix Hennig|Steven Wilson,38940606 -WS-18,WS-18.50,Social media data as a lens onto care-seeking behavior among women veterans of the US armed forces,Kacie Kelly|Alex Fine|Glen Coppersmith,38940615 -WS-18,WS-18.51,Understanding Weekly COVID-19 Concerns through Dynamic Content-Specific LDA Topic Modeling,Mohammadzaman Zamani|H.|Rew Schwartz|Johannes Eichstaedt|Sharath Chandra Guntuku|Adithya Virinchipuram Ganesan|Sean Clouston|Salvatore Giorgi,38940607 -WS-18,WS-18.52,Emoji and Self-Identity in Twitter Bios,Jinhang Li|Giorgos Longinos|Steven Wilson|Walid Magdy,38940622 -WS-18,WS-18.53,Analyzing Gender Bias within Narrative Tropes,Dhruvil Gala|Mohammad Omar Khursheed|Hannah Lerner|Brendan O'Connor|Mohit Iyyer,38940608 -WS-18,WS-18.57,An Unfair Affinity Toward Fairness: Characterizing 70 Years of Social Biases in B^Hollywood,Kunal Khadilkar|Ashiqur KhudaBukhsh,38940627 -WS-19,WS-19.12,Evaluation of Machine Translation Methods applied to Medical Terminologies,Konstantinos Skianis|Yann Briand|Florent Desgrippes,38940042 -WS-19,WS-19.13,Not a cute stroke: Analysis of Rule- and Neural Network-based Information Extraction Systems for Brain Radiology Reports,Andreas Grivas|Beatrice Alex|Claire Grover|Richard Tobin|William Whiteley,38940043 -WS-19,WS-19.14,Biomedical Event Extraction as Multi-turn Question Answering,Xing David Wang|Leon Weber|Ulf Leser,38940044 -WS-19,WS-19.15,GGPONC: A Corpus of German Medical Text with Rich Metadata Based on Clinical Practice Guidelines,Florian Borchert|Christina Lohr|Luise Modersohn|Thomas Langer|Markus Follmann|Jan Philipp Sachs|Udo Hahn|Matthieu-P. Schapranow,38940045 -WS-19,WS-19.17,Medical Concept Normalization in User-Generated Texts by Learning Target Concept Embeddings,Katikapalli Subramanyam Kalyan|Sivanesan Sangeetha,38940046 -WS-19,WS-19.2,The Impact of De-identification on Downstream Named Entity Recognition in Clinical Text,Hanna Berg|Aron Henriksson|Hercules Dalianis,38940038 -WS-19,WS-19.24,Defining and Learning Refined Temporal Relations in the Clinical Narrative,Chen Lin|Kristin Wright-Bettner|Timothy Miller|Steven Bethard|Dmitriy Dligach|Martha Palmer|James H. Martin|Guergana Savova,38940047 -WS-19,WS-19.26,Simple Hierarchical Multi-Task Neural End-To-End Entity Linking for Biomedical Text,Maciej Wiatrak|Juha Iso-Sipila,38940048 -WS-19,WS-19.27,Detection of Mental Health from Reddit via Deep Contextualized Representations,Zhengping Jiang|Sarah Ita Levitan|Jonathan Zomick|Julia Hirschberg,38940049 -WS-19,WS-19.28,Information retrieval for animal disease surveillance: a pattern-based approach,Sarah Valentin|Renaud Lancelot|Mathieu Roche,38940050 -WS-19,WS-19.32,Identifying Personal Experience Tweets of Medication Effects Using Pre-trained RoBERTa Language Model and Its Updating,Minghao Zhu|Youzhe Song|Ge Jin|Keyuan Jiang,38940051 -WS-19,WS-19.35,Detecting Foodborne Illness Complaints in Multiple Languages Using English Annotations Only,Ziyi Liu|Giannis Karamanolakis|Daniel Hsu|Luis Gravano,38940052 -WS-19,WS-19.4,Normalization of Long-tail Adverse Drug Reactions in Social Media,Emmanouil Manousogiannis|Sepideh Mesbah|Alessandro Bozzon|Robert-Jan Sips|Zoltan Szlanik|Selene Baez,38940039 -WS-19,WS-19.44,Context-Aware Automatic Text Simplification of Health Materials in Low-Resource Domains,Tarek Sakakini|Jong Yoon Lee|Aditya Duri|Renato F.L. Azevedo|Victor Sadauskas|Kuangxiao Gu|Suma Bhat|Dan Morrow|James Graumlich|Saqib Walayat|Mark Hasegawa-Johnson|Thomas Huang|Ann Willemsen-Dunlap|Donald Halpin,38940053 -WS-19,WS-19.5,Multitask Learning of Negation and Speculation using Transformers,Aditya Khandelwal|Benita Kathleen Britto,38940040 -WS-19,WS-19.8,An efficient representation of chronological events in medical texts,Andrey Kormilitzin|Nemanja Vaci|Qiang Liu|Hao Ni|Goran Nenadic|Alejo Nevado-Holgado,38940041 -WS-2,WS-2.1,The DiDi Machine Translation System for WMT20,Tanfang Chen|Weiwei Wang|Wenyang Wei|Xing Shi|Xiangang Li|Jieping Ye|Kevin Knight,38939543 -WS-2,WS-2.100,Score Combination for Improved Parallel Corpus Filtering for Low Resource Conditions,Muhammad ElNokrashy|Amr Hendy|Mohamed Abdelghaffar|Mohamed Afify|Ahmed Tawfik|Hany Hassan Awadalla,38939612 -WS-2,WS-2.101,Data Selection for Unsupervised Translation of German–Upper Sorbian,Lukas Edman|Antonio Toral|Gertjan Van Noord,38939613 -WS-2,WS-2.102,Mitigating Gender Bias in Machine Translation with Target Gender Annotations,Artūrs Stafanovičs|Mārcis Pinnis|Toms Bergmanis,38939614 -WS-2,WS-2.103,Infosys Machine Translation System for WMT20 Similar Language Translation Task,Kamalkumar Rathinasamy|Amanpreet Singh|Balaguru Sivasambagupta|Prajna Prasad Neerchal|Vani Sivasankaran,38939615 -WS-2,WS-2.104,The ADAPT System Description for the WMT20 News Translation Task,Venkatesh Parthasarathy|Akshai Ramesh|Rejwanul Haque|Andy Way,38939616 -WS-2,WS-2.105,The ADAPT's Submissions to the WMT20 Biomedical Translation Task,Prashant Nayak|Rejwanul Haque|Andy Way,38939617 -WS-2,WS-2.107,LIMSI @ WMT 2020,Sadaf Abdul Rauf|José Carlos Rosales Núñez|Minh Quang Pham|François Yvon,38939618 -WS-2,WS-2.109,Russian-English Bidirectional Machine Translation System,Ariel Xv,38939619 -WS-2,WS-2.113,Transfer Learning for Related Languages: IIT Delhi's Submissions to the WMT20 Similar Language Translation Task,Lovish Madaan|Soumya Sharma|Parag Singla,38939620 -WS-2,WS-2.114,Adobe AMPS's Submission for Very Low Resource Supervised Translation Task at WMT20,Keshaw Singh,38939621 -WS-2,WS-2.115,Alibaba's Submission for the WMT 2020 APE Shared Task: Improving Automatic Post-Editing with Pre-trained Conditional Cross-Lingual BERT,Jiayi Wang|Ke Wang|Kai Fan|Yuqi Zhang|Jun Lu|Xin Ge|Yangbin Shi|Yu Zhao,38939622 -WS-2,WS-2.116,NMT based Similar Language Translation for Hindi - Marathi,Vandan Mujadia|Dipti Sharma,38939623 -WS-2,WS-2.117,Facebook AI's WMT20 News Translation Task Submission,Peng-Jen Chen|Ann Lee|Changhan Wang|Naman Goyal|Angela Fan|Mary Williamson|Jiatao Gu,38939624 -WS-2,WS-2.118,NLPRL System for Very Low Resource Supervised Machine Translation,Rupjyoti Baruah|Rajesh Kumar Mundotiya|Amit Kumar|Anil Kumar Singh,38939625 -WS-2,WS-2.119,How Should Markup Tags Be Translated?,Greg Hanneman|Georgiana Dinu,38939626 -WS-2,WS-2.12,Complete Multilingual Neural Machine Translation,Markus Freitag|Orhan Firat,38939550 -WS-2,WS-2.121,The University of Edinburgh-Uppsala University's Submission to the WMT 2020 Chat Translation Task,Nikita Moghe|Christian Hardmeier|Rachel Bawden,38939627 -WS-2,WS-2.122,RTM Ensemble Learning Results at Quality Estimation Task,Ergun Biçici,38939628 -WS-2,WS-2.123,JUST System for WMT20 Chat Translation Task,Roweida Mohammed|Mahmoud Al-Ayyoub|Malak Abdullah,38939629 -WS-2,WS-2.124,BERGAMOT-LATTE Submissions for the WMT20 Quality Estimation Shared Task,Marina Fomicheva|Shuo Sun|Lisa Yankovskaya|Frédéric Blain|Vishrav Chaudhary|Mark Fishel|Francisco Guzmán|Lucia Specia,38939630 -WS-2,WS-2.125,Dual Conditional Cross Entropy Scores and LASER Similarity Scores for the WMT20 Parallel Corpus Filtering Shared Task,Felicia Koerner|Philipp Koehn,38939631 -WS-2,WS-2.128,UoS Participation in the WMT20 Translation of Biomedical Abstracts,Felipe Soares|Delton Vaz,38939632 -WS-2,WS-2.133,Tilde at WMT 2020: News Task Systems,Rihards Krišlauks|Mārcis Pinnis,38939633 -WS-2,WS-2.135,NRC Systems for Low Resource German-Upper Sorbian Machine Translation 2020: Transfer Learning with Lexical Modifications,Rebecca Knowles|Samuel Larkin|Darlene Stewart|Patrick Littell,38939634 -WS-2,WS-2.136,Linguistically Motivated Subwords Improve English-Tamil Translation: University of Groningen's Submission to WMT-2020,Prajit Dhar|Arianna Bisazza|Gertjan Van Noord,38939635 -WS-2,WS-2.137,The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT,Jörg Tiedemann,38939636 -WS-2,WS-2.138,Priming Neural Machine Translation,Minh Quang Pham|Jitao Xu|Josep Crego|François Yvon|Jean Senellart,38939637 -WS-2,WS-2.139,NUIG-Panlingua-KMI Hindi↔Marathi MT Systems for Similar Language Translation Task @ WMT 2020,Atul Kr. Ojha|Priya Rani|Akanksha Bansal|Bharathi Raja Chakravarthi|Ritesh Kumar|John P. McCrae,38939638 -WS-2,WS-2.14,Combining Sequence Distillation and Transfer Learning for Efficient Low-Resource Neural Machine Translation Models,Raj Dabre|Atsushi Fujita,38939551 -WS-2,WS-2.141,NRC Systems for the 2020 Inuktitut-English News Translation Task,Rebecca Knowles|Darlene Stewart|Samuel Larkin|Patrick Littell,38939639 -WS-2,WS-2.142,Translating Similar Languages: Role of Mutual Intelligibility in Multilingual Transformers,Ife Adebara|El Moatez Billah Nagoudi|Muhammad Abdul Mageed,38939640 -WS-2,WS-2.143,CUNI Systems for the Unsupervised and Very Low Resource Translation Task in WMT20,Ivana Kvapilíková|Tom Kocmi|Ondřej Bojar,38939641 -WS-2,WS-2.144,The University of Edinburgh's English-Tamil and English-Inuktitut Submissions to the WMT20 News Translation Task,Rachel Bawden|Alexandra Birch|Radina Dobreva|Arturo Oncevay|Antonio Valerio Miceli Barone|Philip Williams,38939642 -WS-2,WS-2.146,IST-Unbabel Participation in the WMT20 Quality Estimation Shared Task,João Moura|Miguel Vera|Daan Van Stigt|Fabio Kepler|André F. T. Martins,38939643 -WS-2,WS-2.147,YerevaNN's systems for WMT20 Biomedical Translation Task: The effect of fixing misaligned sentence pairs,Karen Hambardzumyan|Hovhannes Tamoyan|Hrant Khachatrian,38939644 -WS-2,WS-2.149,Lite Training Strategies for Portuguese-English and English-Portuguese Translation,Alexandre Lopes|Rodrigo Nogueira|Roberto Lotufo|Helio Pedrini,38939645 -WS-2,WS-2.150,Noising Scheme for Data Augmentation in Automatic Post-Editing,WonKee Lee|Jaehun Shin|Baikjin Jung|Jihyung Lee|Jong-Hyeok Lee,38939646 -WS-2,WS-2.153,The University of Maryland's Submissions to the WMT20 Chat Translation Task: Searching for More Data to Adapt Discourse-Aware Neural Machine Translation,Calvin Bao|Yow-Ting Shiue|Chujun Song|Jie Li|Marine Carpuat,38939647 -WS-2,WS-2.155,The AFRL WMT20 News-Translation Systems,Jeremy Gwinnup|Tim Anderson,38939648 -WS-2,WS-2.156,An exploratory approach to the Parallel Corpus Filtering shared task WMT20,Ankur Kejriwal|Philipp Koehn,38939649 -WS-2,WS-2.157,Incorporating Terminology Constraints in Automatic Post-Editing,David Wan|Chris Kedzie|Faisal Ladhak|Marine Carpuat|Kathleen McKeown,38939650 -WS-2,WS-2.158,NJU's submission for WMT2020 QE Shared Task,Qu Cui|Xiang Geng|Shujian Huang|Jiajun Chen,38939651 -WS-2,WS-2.16,Paraphrase Generation as Zero-Shot Multilingual Translation: Disentangling Semantic Similarity from Lexical and Syntactic Diversity,Brian Thompson|Matt Post,38939552 -WS-2,WS-2.160,"Evaluating Low Resource Machine Translation: BLEU, character-based or semantic metric?",Chi-kiu Lo,38939652 -WS-2,WS-2.161,MT reference-less evaluation using YiSi-2 with bilingual mappings of massive multilingual language model learnt from minimal amount of parallel data,Chi-kiu Lo|Samuel Larkin,38939653 -WS-2,WS-2.162,Contact Relatedness can help improve multilingual NMT: Microsoft STCI-MT @ WMT20,Vikrant Goyal|Anoop Kunchukuttan|Rahul Kejriwal|Siddharth Jain|Amit Bhagwat,38939654 -WS-2,WS-2.163,A Study of Residual Adapters for Multi-Domain Neural Machine Translation,Minh Quang Pham|Josep Maria Crego|François Yvon|Jean Senellart,38939655 -WS-2,WS-2.167,FJWU participation for the WMT20 Biomedical Translation Task,Sumbal Naz|Sadaf Abdul Rauf|Noor-e- Hira|Sami Ul Haq,38939656 -WS-2,WS-2.168,SJTU-NICT's Supervised and Unsupervised Neural Machine Translation Systems for the WMT20 News Translation Task,Zuchao Li|Hai Zhao|Rui Wang|Kehai Chen|Masao Utiyama|Eiichiro Sumita,38939657 -WS-2,WS-2.169,Iteratively refined statistical sentence alignment and improved bilingualmappings of pretrained multilingual language model for identifying betterparallel MT training data,Chi-kiu Lo|Eric Joanis,38939658 -WS-2,WS-2.170,Gender Coreference and Bias Evaluation at WMT 2020,Tom Kocmi|Tomasz Limisiewicz|Gabriel Stanovsky,38939659 -WS-2,WS-2.171,Subword Segmentation and a Single Bridge Language Affect Zero-Shot Neural Machine Translation,Annette Rios|Mathias Müller|Rico Sennrich,38939660 -WS-2,WS-2.172,"Speed-optimized, Compact Student Models that Distill Knowledge from a Larger Teacher Model: the UEDIN-CUNI Submission to the WMT 2020 News Translation Task",Ulrich Germann|Roman Grundkiewicz|Martin Popel|Radina Dobreva|Nikolay Bogoychev|Kenneth Heafield,38939661 -WS-2,WS-2.175,PROMT Systems for WMT 2020 Shared News Translation Task,Alexander Molchanov,38939662 -WS-2,WS-2.177,The University of Edinburgh's submission to the German-to-English and English-to-German Tracks in the WMT 2020 News Translation and Zero-shot Translation Robustness Tasks,Ulrich Germann,38939663 -WS-2,WS-2.178,Tencent AI Lab Machine Translation Systems for the WMT20 Biomedical Translation Task,Xing Wang|Zhaopeng Tu|Longyue Wang|Shuming Shi,38939664 -WS-2,WS-2.179,"Machine Translation for English–Inuktitut with Segmentation,Data Acquisition and Pre-Training",Christian Roest|Lukas Edman|Gosse Minnema|Kevin Kelly|Jennifer Spenader|Antonio Toral,38939665 -WS-2,WS-2.18,Fine-grained linguistic evaluation for state-of-the-art Machine Translation,Eleftherios Avramidis|Vivien Macketanz|Ursula Strohriegel|Aljoscha Burchardt|Sebastian Möller,38939553 -WS-2,WS-2.180,CUNI Submission for Inuktitut Language in WMT News 2020,Tom Kocmi,38939666 -WS-2,WS-2.181,The WMT 2020 Shared Tasks in Unsupervised MT and Very Low Resource Supervised MT,Alexander Fraser,38939667 -WS-2,WS-2.183,CUNI English-Czech and English-Polish Systems in WMT20: Robust Document-Level Training,Martin Popel,38939668 -WS-2,WS-2.185,Zero-Shot Translation Quality Estimation with Explicit Cross-Lingual Patterns,Lei Zhou|Liang Ding|Koichi Takeda,38939669 -WS-2,WS-2.187,Tencent Neural Machine Translation Systems for the WMT20 News Translation Task,Shuangzhi Wu|Xing Wang|Longyue Wang|Fangxu Liu|Jun Xie|Zhaopeng Tu|Shuming Shi|Mu Li,38939670 -WS-2,WS-2.188,Tencent AI Lab Machine Translation Systems for WMT20 Chat Translation Task,Longyue Wang|Zhaopeng Tu|Xing Wang|Li Ding|Liang Ding|Shuming Shi,38939671 -WS-2,WS-2.189,Findings of the WMT 2020 Shared Task on Automatic Post-Editing,Rajen Chatterjee|Markus Freitag|Matteo Negri|Marco Turchi,38939672 -WS-2,WS-2.19,When Does Unsupervised Machine Translation Work?,Kelly Marchisio|Kevin Duh|Philipp Koehn,38939554 -WS-2,WS-2.190,ParBLEU: Augmenting Metrics with Automatic Paraphrases for the WMT'20 Metrics Shared Task,Rachel Bawden|Biao Zhang|Andre Tättar|Matt Post,38939673 -WS-2,WS-2.191,Findings of the WMT 2020 Shared Task on Chat Translation,M. Amin Farajian|António V. Lopes|André F. T. Martins|Sameen Maruf|Gholamreza Haffari,38939674 -WS-2,WS-2.192,"Findings of the WMT 2020 Biomedical Translation Shared Task: Basque, Italian and Russian as New Additional Languages",Rachel Bawden|Giorgio Maria Di Nunzio|Cristian Grozea|Inigo Jauregi Unanue|Antonio Jimeno Yepes|Nancy Mah|David Martinez|Aurélie Névéol|Mariana Neves|Maite Oronoz|Olatz Perez-de-Viñaspre|Massimo Piccardi|Roland Roller|Amy Siu|Philippe Thomas|Federica Vezzani|Maika Vicente Navarro|Dina Wiemann|Lana Yeganova,38939675 -WS-2,WS-2.193,Findings of the WMT 2020 Shared Task on Machine Translation Robustness,Lucia Specia|Zhenhao Li|Juan Pino|Vishrav Chaudhary|Francisco Guzmán|Graham Neubig|Nadir Durrani|Yonatan Belinkov|Philipp Koehn|Hassan Sajjad|Paul Michel|Xian Li,38939676 -WS-2,WS-2.194,Findings of the WMT 2020 Shared Task on Quality Estimation,Lucia Specia|Frédéric Blain|Marina Fomicheva|Erick Fonseca|Vishrav Chaudhary|Francisco Guzmán|André F. T. Martins,38939677 -WS-2,WS-2.195,Findings of the WMT 2020 Shared Task on Parallel Corpus Filtering and Alignment,Philipp Koehn|Vishrav Chaudhary|Ahmed El-Kishky|Naman Goyal|Peng-Jen Chen|Francisco Guzmán,38939678 -WS-2,WS-2.196,Findings of the First Shared Task on Lifelong Learning Machine Translation,Loïc Barrault|Magdalena Biesialska|Marta R. Costa-jussà|Fethi Bougares|Olivier Galibert,38939679 -WS-2,WS-2.197,Findings of the 2020 Conference on Machine Translation (WMT20),Loïc Barrault|Magdalena Biesialska|Ondřej Bojar|Marta R. Costa-jussà|Christian Federmann|Yvette Graham|Roman Grundkiewicz|Barry Haddow|Matthias Huck|Eric Joanis|Tom Kocmi|Philipp Koehn|Chi-kiu Lo|Nikola Ljubešić|Christof Monz|Makoto Morishita|Masaaki Nagata|Toshiaki Nakazawa|Santanu Pal|Matt Post|Marcos Zampieri,38939680 -WS-2,WS-2.1971,Findings of the 2020 Conference on Machine Translation (WMT20): News Translation Task,Christian Federmann|Yvette Graham|Roman Grundkiewicz|Barry Haddow|Tom Kocmi,38940635 -WS-2,WS-2.1972,Findings of the 2020 Conference on Machine Translation (WMT20): Similar Language Translation Task,Magdalena Biesialska|Marta R. Costa-jussà|Nikola Ljubešić|Santanu Pal|Marcos Zampieri,38940636 -WS-2,WS-2.1973,Findings of the 2020 Conference on Machine Translation (WMT20): Test Suites,Ondřej Bojar,38940637 -WS-2,WS-2.198,Learning to Evaluate Translation Beyond English: BLEURT Submissions to the WMT Metrics 2020 Shared Task,Thibault Sellam|Amy Pu|Hyung Won Chung|Sebastian Gehrmann|Qijun Tan|Markus Freitag|Dipanjan Das|Ankur Parikh,38939681 -WS-2,WS-2.2,Volctrans Parallel Corpus Filtering System for WMT 2020,Runxin Xu|Zhuo Zhi|Jun Cao|Mingxuan Wang|Lei Li,38939544 -WS-2,WS-2.20,"Multilingual Neural Machine Translation: Case-study for Catalan, Spanish and Portuguese Romance Languages",Pere Vergés Boncompte|Marta R. Costa-jussà,38939555 -WS-2,WS-2.21,NICT Kyoto Submission for the WMT'20 Quality Estimation Task: Intermediate Training for Domain and Task Adaptation,Raphael Rubino,38939556 -WS-2,WS-2.22,On the Same Page? Comparing Inter-Annotator Agreement in Sentence and Document Level Human Machine Translation Evaluation,Sheila Castilho,38939557 -WS-2,WS-2.23,OPPO's Machine Translation Systems for WMT20,Tingxun Shi|Shiyu Zhao|Xiaopu Li|Xiaoxue Wang|Qian Zhang|Di Ai|Dawei Dang|Xue Zhengshan|Jie Hao,38939558 -WS-2,WS-2.26,Towards Multimodal Simultaneous Neural Machine Translation,Aizhan Imankulova|Masahiro Kaneko|Tosho Hirasawa|Mamoru Komachi,38939559 -WS-2,WS-2.3,Tohoku-AIP-NTT at WMT 2020 News Translation Task,Shun Kiyono|Takumi Ito|Ryuto Konno|Makoto Morishita|Jun Suzuki,38939545 -WS-2,WS-2.31,Document-aligned Japanese-English Conversation Parallel Corpus,Matīss Rikters|Ryokan Ri|Tong Li|Toshiaki Nakazawa,38939560 -WS-2,WS-2.32,POSTECH-ETRI's Submission to the WMT2020 APE Shared Task:Automatic Post-Editing with Cross-lingual Language Model,Jihyung Lee|WonKee Lee|Jaehun Shin|Baikjin Jung|Young-Kil Kim|Jong-Hyeok Lee,38939561 -WS-2,WS-2.35,Pretrained Language Models and Backtranslation for English-Basque Biomedical Neural Machine Translation,Inigo Jauregi Unanue|Massimo Piccardi,38939562 -WS-2,WS-2.36,WMT20 Document-Level Markable Error Exploration,Vilém Zouhar|Tereza Vojtěchová|Ondřej Bojar,38939563 -WS-2,WS-2.37,The LMU Munich System for the WMT20 Very Low Resource Supervised MT Task,Jindřich Libovický|Viktor Hangya|Helmut Schmid|Alexander Fraser,38939564 -WS-2,WS-2.39,Incorporate Semantic Structures into Machine Translation Evaluation via UCCA,Jin Xu|Yinuo Guo|Junfeng Hu,38939565 -WS-2,WS-2.40,Samsung R D Institute Poland submission to WMT20 News Translation Task,Mateusz Krubiński|Marcin Chochowski|Bartłomiej Boczek|Mikołaj Koszowski|Adam Dobrowolski|Marcin Szymański|Paweł Przybysz,38939566 -WS-2,WS-2.42,The MUCOW word sense disambiguation test suite at WMT 2020,Yves Scherrer|Alessandro Raganato|Jörg Tiedemann,38939567 -WS-2,WS-2.43,Language Models not just for Pre-training: Fast Online Neural Noisy Channel Modeling,Shruti Bhosale|Kyra Yee|Sergey Edunov|Michael Auli,38939568 -WS-2,WS-2.44,Bicleaner at WMT 2020: Universitat d'Alacant–Prompsit's submission to the parallel corpus filtering shared task,Miquel Esplà-Gomis|Víctor M. Sánchez-Cartagena|Jaume Zaragoza-Bernabeu|Felipe Sánchez-Martínez,38939569 -WS-2,WS-2.45,HW-TSC's Participation at WMT 2020 Automatic Post Editing Shared Task,Hao Yang|Minghan Wang|Daimeng Wei|Hengchao Shang|Jiaxin Guo|Zongyao Li|Lizhi Lei|Ying Qin|Shimin Tao|Shiliang Sun|Yimeng Chen,38939570 -WS-2,WS-2.46,HW-TSC's Participation at WMT 2020 Quality Estimation Shared Task,Minghan Wang|Hao Yang|Hengchao Shang|Daimeng Wei|Jiaxin Guo|Lizhi Lei|Ying Qin|Shimin Tao|Shiliang Sun|Yimeng Chen|Liangyou Li,38939571 -WS-2,WS-2.47,The NiuTrans Machine Translation Systems for WMT20,Yuhao Zhang|Ziyang Wang|Runzhe Cao|Binghao Wei|Weiqiao Shan|Shuhan Zhou|Abudurexiti Reheman|Tao Zhou|Xin Zeng|Laohu Wang|Yongyu Mu|Jingnan Zhang|Xiaoqian Liu|Xuanjun Zhou|Yinqiao Li|Bei Li|Tong Xiao|Jingbo Zhu,38939572 -WS-2,WS-2.48,HW-TSC's Participation in the WMT 2020 News Translation Shared Task,Daimeng Wei|Hengchao Shang|Zhanglin Wu|Zhengzhe Yu|Liangyou Li|Jiaxin Guo|Minghan Wang|Hao Yang|Lizhi Lei|Ying Qin|Shiliang Sun,38939573 -WS-2,WS-2.49,eTranslation's Submissions to the WMT 2020 News Translation Task,Csaba Oravecz|Katina Bontcheva|László Tihanyi|David Kolovratnik|Bhavani Bhaskar|Adrien Lardilleux|Szymon Klocek|Andreas Eisele,38939574 -WS-2,WS-2.5,Two-Phase Cross-Lingual Language Model Fine-Tuning for Machine Translation Quality Estimation,Dongjun Lee,38939546 -WS-2,WS-2.51,The NITS-CNLP System for the Unsupervised MT Task at WMT 2020,Salam Michael Singh|Thoudam Doren Singh|Sivaji Bandyopadhyay,38939575 -WS-2,WS-2.52,Huawei's Submissions to the WMT20 Biomedical Translation Task,Wei Peng|Jianfeng Liu|Minghan Wang|Liangyou Li|Xupeng Meng|Hao Yang|Qun Liu,38939576 -WS-2,WS-2.53,"Naver Labs Europe's Participation to the Robustness, Chat and Biomedical Tasks at WMT 2020",Alexandre Berard|Ioan Calapodescu|Vassilina Nikoulina|Jerin Philip,38939577 -WS-2,WS-2.54,Neural Machine Translation between similar South-Slavic languages,Maja Popović|Alberto Poncelas,38939578 -WS-2,WS-2.57,IIE's Neural Machine Translation Systems for WMT20,Xiangpeng Wei|Ping Guo|Yunpeng Li|Xingsheng Zhang|Luxi Xing|Yue Hu,38939579 -WS-2,WS-2.58,Alibaba Submission to the WMT20 Parallel Corpus Filtering Task,Jun Lu|Xin Ge|Yangbin Shi|Yuqi Zhang,38939580 -WS-2,WS-2.59,The Volctrans Machine Translation System for WMT20,Liwei Wu|Xiao Pan|Zehui Lin|Yaoming Zhu|Mingxuan Wang|Lei Li,38939581 -WS-2,WS-2.6,Cross-Lingual Transformers for Neural Automatic Post-Editing,Dongjun Lee,38939547 -WS-2,WS-2.60,The LMU Munich System for the WMT 2020 Unsupervised Machine Translation Shared Task,Alexandra Chronopoulou|Dario Stojanovski|Viktor Hangya|Alexander Fraser,38939582 -WS-2,WS-2.62,Addressing Exposure Bias With Document Minimum Risk Training: Cambridge at the WMT20 Biomedical Translation Task,Danielle Saunders|Bill Byrne,38939583 -WS-2,WS-2.65,UdS-DFKI@WMT20: Unsupervised MT and Very Low Resource Supervised MT for German↔Upper Sorbian,Sourav Dutta|Jesujoba Alabi|Saptarashmi Bandyopadhyay|Dana Ruiter|Josef Van Genabith,38939584 -WS-2,WS-2.68,Diving Deep into Context-Aware Neural Machine Translation,Jingjing Huo|Christian Herold|Yingbo Gao|Leonard Dahlmann|Shahram Khadivi|Hermann Ney,38939585 -WS-2,WS-2.69,The DeepMind Chinese–English Document Translation System at WMT2020,Lei Yu|Laurent Sartran|Po-Sen Huang|Wojciech Stokowiec|Domenic Donato|Srivatsan Srinivasan|Alek Andreev|Wang Ling|Sona Mokra|Agustin Dal Lago|Yotam Doron|Susannah Young|Phil Blunsom|Chris Dyer,38939586 -WS-2,WS-2.70,Neural Machine Translation for Similar Language Translation: The Case of Indo-Aryan Languages,Santanu Pal|Marcos Zampieri,38939587 -WS-2,WS-2.71,Fast Interleaved Bidirectional Sequence Generation,Biao Zhang|Ivan Titov|Rico Sennrich,38939588 -WS-2,WS-2.72,The University of Helsinki and Aalto University submissions to the WMT 2020 news and low-resource translation tasks,Yves Scherrer|Stig-Arne Grönroos|Sami Virpioja,38939589 -WS-2,WS-2.73,A3-108 Machine Translation System for Similar Language Translation Shared Task 2020,Saumitra Yadav|Manish Shrivastava,38939590 -WS-2,WS-2.74,Elhuyar submission to the Biomedical Translation Task 2020 onterminology and abstracts translation,Ander Corral|Xabier Saralegi,38939591 -WS-2,WS-2.75,Attention Transformer Model for Translation of Similar Languages,Farhan Dhanani|Muhammad Rafi,38939592 -WS-2,WS-2.76,Human-Paraphrased References Improve Neural Machine Translation,Markus Freitag|George Foster|David Grangier|Colin Cherry,38939593 -WS-2,WS-2.77,The TALP-UPC Machine Translation Systems for WMT20 News Translation Task: Multilingual Adaptation for Low Resource MT,Carlos Escolano|Marta R. Costa-jussà|José A. R. Fonollosa,38939594 -WS-2,WS-2.8,Towards a Better Evaluation of Metrics for Machine Translation,Peter Stanchev|Weiyue Wang|Hermann Ney,38939548 -WS-2,WS-2.80,The IPN-CIC team system submission for the WMT 2020 simmilar language task,Luis A. Menéndez-Salazar|Grigori Sidorov|Marta R. Costa-Jussà,38939595 -WS-2,WS-2.81,WeChat Neural Machine Translation Systems for WMT20,Fandong Meng|Jianhao Yan|Yijin Liu|Yuan Gao|Xianfeng Zeng|Qinsong Zeng|Peng Li|Ming Chen|Jie Zhou|Sifan Liu|Hao Zhou,38939596 -WS-2,WS-2.82,Look It Up: Bilingual and Monolingual Dictionaries Improve Neural Machine Translation,Xing Jie Zhong|David Chiang,38939597 -WS-2,WS-2.83,Low-Resource Translation as Language Modeling,Tucker Berckmann|Berkan Hiziroglu,38939598 -WS-2,WS-2.84,An Iterative Knowledge Transfer NMT System for WMT20 News Translation Task,Jiwan Kim|Soyoon Park|Sangha Kim|Yoonjung Choi,38939599 -WS-2,WS-2.86,Combination of Neural Machine Translation Systems at WMT20,Benjamin Marie|Raphael Rubino|Atsushi Fujita,38939600 -WS-2,WS-2.87,The NiuTrans System for the WMT20 Quality Estimation Shared Task,Chi Hu|Hui Liu|Kai Feng|Chen Xu|Nuo Xu|Zefan Zhou|Shiqin Yan|Yingfeng Luo|Chenglong Wang|Xia Meng|Tong Xiao|Jingbo Zhu,38939601 -WS-2,WS-2.88,TMUOU submission for WMT20 Quality Estimation Shared Task,Akifumi Nakamachi|Hiroki Shimanaka|Tomoyuki Kajiwara|Mamoru Komachi,38939602 -WS-2,WS-2.89,GTCOM Neural Machine Translation Systems for WMT20,Chao Bei|Hao Zong|Qingmin Liu|Conghu Yuan,38939603 -WS-2,WS-2.9,Ixamed's submission description for WMT20 Biomedical shared task: benefits and limitations of using terminologies for domain adaptation,Xabier Soto|Olatz Perez-de-Viñaspre|Gorka Labaka|Maite Oronoz,38939549 -WS-2,WS-2.90,Unbabel's Participation in the WMT20 Metrics Shared Task,Ricardo Rei|Craig Stewart|Ana C Farinha|Alon Lavie,38939604 -WS-2,WS-2.91,Transformer-based Neural Machine Translation System for Hindi - Marathi,Amit Kumar|Rupjyoti Baruah|Rajesh Kumar Mundotiya|Anil Kumar Singh,38939605 -WS-2,WS-2.92,Filtering Noisy Parallel Corpus using Transformers with Proxy Task Learning,Haluk Açarçiçek|Talha Çolakoğlu|pınar Ece Aktan hatipoğlu|Chong Hsuan Huang|Wei Peng,38939606 -WS-2,WS-2.93,TransQuest at WMT2020: Sentence-Level Direct Assessment,Tharindu Ranasinghe|Constantin Orasan|Ruslan Mitkov,38939607 -WS-2,WS-2.94,Document Level NMT of Low-Resource Languages with Backtranslation,Sami Ul Haq|Sadaf Abdul Rauf|Arsalan Shaukat|Abdullah Saeed,38939608 -WS-2,WS-2.96,Tencent submission for WMT20 Quality Estimation Shared Task,Haijiang Wu|Zixuan Wang|Qingsong Ma|Xinjie Wen|Ruichen Wang|Xiaoli Wang|Yulin Zhang|Zhipeng Yao|Siyao Peng,38939609 -WS-2,WS-2.97,PATQUEST: Papago Translation Quality Estimation,Yujin Baek|Zae Myung Kim|Jihyung Moon|Hyunjoong Kim|Eunjeong Park,38939610 -WS-2,WS-2.99,Hindi-Marathi Cross Lingual Model,Sahinur Rahman Laskar|Abdullah Faiz Ur Rahman Khilji|Partha Pakray|Sivaji Bandyopadhyay,38939611 -WS-20,WS-20.13,"Probabilistic Extension of Precision, Recall, and F1 Score for More Thorough Evaluation of Classification Models",Reda Yacouby|Dustin Axman,38939710 -WS-20,WS-20.15,Truth or Error? Towards systematic analysis of factual errors in abstractive summaries,Klaus-Michael Lux|Maya Sappelli|Martha Larson,38939711 -WS-20,WS-20.16,Evaluating Word Embeddings on Low-Resource Languages,Nathan Stringham|Mike Izbicki,38939712 -WS-20,WS-20.1663,Automatically Identifying Gender Issues in Machine Translation using Perturbations,Hila Gonen|Kellie Webster,38940033 -WS-20,WS-20.18,"Best Practices for Crowd-based Evaluation of German Summarization: Comparing Crowd, Expert and Automatic Evaluation",Neslihan Iskender|Tim Polzehl|Sebastian Möller,38939713 -WS-20,WS-20.183,GRUEN for Evaluating Linguistic Quality of Generated Text,Wanzheng Zhu|Suma Bhat,38940645 -WS-20,WS-20.21,Fill in the BLANC: Human-free quality estimation of document summaries,Oleg Vasilyev|Vedant Dharnidharka|John Bohannon,38939714 -WS-20,WS-20.22,Are Some Words Worth More than Others?,Shiran Dudy|Steven Bedrick,38939715 -WS-20,WS-20.23,A survey on Recognizing Textual Entailment as an NLP Evaluation,Adam Poliak,38939716 -WS-20,WS-20.2311,An Evaluation Method for DiachronicWord Sense Induction,Ashjan Alsulaimani|Erwan Moreau|Carl Vogel,38940034 -WS-20,WS-20.2378,KoBE: Knowledge-Based Machine Translation Evaluation,Zorik Gekhman|Roee Aharoni|Genady Beryozkin|Markus Freitag|Wolfgang Macherey,38940035 -WS-20,WS-20.25,One of these words is not like the other: a reproduction of outlier identification using non-contextual word representations,Jesper Brink Andersen|Mikkel Bak Bertelsen|Mikkel Hørby Schou|Manuel R. Ciosici|Ira Assent,38939717 -WS-20,WS-20.2740,CDEvalSumm: An Empirical Study of Cross-Dataset Evaluation for Neural Summarization Systems,Yiran Chen|Pengfei Liu|Ming Zhong|Zi-Yi Dou|DanqingWang|Xipeng Qiu|Xuanjing Huang,38940036 -WS-20,WS-20.28,Evaluating the Evaluation of Diversity in Natural Language Generation,Guy Tevet|Jonathan Berant,38940785 -WS-20,WS-20.29,Item Response Theory for Efficient Human Evaluation of Chatbots ,João Sedoc|Lyle Ungar,38939718 -WS-20,WS-20.3,Artemis: A Novel Annotation Methodology for Indicative Single Document Summarization,Rahul Jha|Keping Bi|Yang Li|Mahdi Pakdaman|Asli Celikyilmaz|Ivan Zhiboedov|Kieran McDonald,38939707 -WS-20,WS-20.30,ViLBERTScore: Evaluating Image Caption Using Vision-and-Language BERT,Hwanhee Lee|Seunghyun Yoon|Franck Dernoncourt|Doo Soon Kim|Trung Bui|Kyomin Jung,38939719 -WS-20,WS-20.3117,#TurkihTweets: A Benchmark Dataset for Turkish Text Correction,Asiye Tuba Köksal|Özge Bozal|Emre Yürekli|Gizem Gezici,38940037 -WS-20,WS-20.3148,Multichannel Generative Language Model: Learning All Possible Factorizations Within and Across Channels,Harris Chan,38940114 -WS-20,WS-20.34,On Aligning OpenIE Extractions with Knowledge Bases: A Case Study,Kiril Gashteovski|Rainer Gemulla|Bhushan Kotnis|Sven Hertling|Christian Meilicke,38939720 -WS-20,WS-20.35,Improving Text Generation Evaluation with Batch Centering and Tempered Word Mover Distance,Xi Chen|Nan Ding|Tomer Levinboim|Radu Soricut,38939721 -WS-20,WS-20.36,On the Evaluation of Machine Translation n-best Lists,Jacob Bremerman|Huda Khayrallah|Douglas Oard|Matt Post,38939722 -WS-20,WS-20.37,Grammaticality and Language Modelling ,Jingcheng Niu|Gerald Penn,38939723 -WS-20,WS-20.5,ClusterDataSplit: Exploring Challenging Clustering-Based Data Splits for Model Performance Evaluation,Hanna Wecker|Annemarie Friedrich|Heike Adel,38939708 -WS-20,WS-20.7,BLEU Neighbors: A Reference-less Approach to Automatic Evaluation,Kawin Ethayarajh|Dorsa Sadigh,38939709 -WS-20,WS-20.8,An Open-Source Library for Using and Developing Summarization Evaluation Metrics,Daniel Deutsch|Dan Roth,38940784 -WS-20,WS-20.815,A Study in Improving BLEU Reference Coverage with Diverse Automatic Paraphrasing,Rachel Bawden|Biao Zhang|Lisa Yankovskaya|Andre Tättar|Matt Post,38940032 -WS-21,WS-21.10,Structured Prediction for Joint Class Cardinality and Entity Property Inference in Model-Complete Text Comprehension,Hendrik Ter Horst|Philipp Cimiano,38940161 -WS-21,WS-21.11,Cascaded Text Generation with Markov Transformers,Yuntian Deng|Alexander Rush,38940151 -WS-21,WS-21.12,Scaling Hidden Markov Language Models,Justin Chiu|Alexander Rush,38940160 -WS-21,WS-21.14,Energy-based Neural Modelling for Large-Scale Multiple Domain Dialogue State Tracking,Anh Duong Trinh|Robert J. Ross|John D. Kelleher,38940154 -WS-21,WS-21.1463,Recurrent Inference in Text Editing,Ning Shi|Ziheng Zeng|Haotian Zhang|Yichen Gong,38940648 -WS-21,WS-21.16,End-to-End Extraction of Structured Information from Business Documents with Pointer-Generator Networks,Clément Sage|Alex Aussem|Véronique Eglin|Haytham Elghazel|Jérémy Espinas,38940153 -WS-21,WS-21.17,Syntactically restricted self-attention for Semantic Role Labeling,Zihao Deng|Sijia Wang|Brendan Juba,38940162 -WS-21,WS-21.1774,Computer Assisted Translation with Neural Quality Estimation and Automatic Post-Editing,Ke Wang|Jiayi Wang|Niyu Ge|Yangbin Shi|Zhao Yu|Kai Fan,38940649 -WS-21,WS-21.19,Randomized Deep Structured Prediction for Argumentation Mining,Manuel Widmoser|Maria Pacheco|Jean Honorio|Dan Goldwasser,38940158 -WS-21,WS-21.2,Syntax-driven Iterative Expansion Language Models for Controllable Text Generation,Noe Casas|José A. R. Fonollosa|Marta R. Costa-jussà,38940163 -WS-21,WS-21.20,Layer-wise Guided Training for BERT: Learning Incrementally Refined Document Representations,Nikolaos Manginas|Ilias Chalkidis|Prodromos Malakasiotis,38940156 -WS-21,WS-21.21,Improving Joint Training of Inference Networks and Structured Prediction Energy Networks,Lifu Tu|Richard Yuanzhe Pang|Kevin Gimpel,38940143 -WS-21,WS-21.2146,Graph-to-Tree Neural Networks for Learning Structured Input-Output Translation with Applications to Semantic Parsing and Math Word Problem,Shucheng Li|Lingfei Wu|Shiwei Feng|Fangli Xu|Fengyuan Xu|Sheng Zhong,38940650 -WS-21,WS-21.22,Reading the Manual: Event Extraction as Definition Comprehension,Yunmo Chen|Tongfei Chen|Seth Ebner|Aaron Steven White|Benjamin Van Durme,38940159 -WS-21,WS-21.2220,An Instance Level Approach for Shallow Semantic Parsing in Scientific Procedural Text,Daivik Swarup|Ahsaas Bajaj|Sheshera Mysore|Tim O'Gorman|Rajarshi Das|Andrew McCallum,38940651 -WS-21,WS-21.2284,On Long-Tailed Phenomena in Neural Machine Translation,Vikas Raunak|Siddharth Dalmia|Vivek Gupta|Florian Metze,38940652 -WS-21,WS-21.23,On the Discrepancy between Density Estimation and Sequence Generation,Jason Lee|Dustin Tran|Orhan Firat|Kyunghyun Cho,38940144 -WS-21,WS-21.24,Log-Linear Reformulation of the Noisy Channel Model for Document-Level Neural Machine Translation,Sébastien Jean|Kyunghyun Cho,38940157 -WS-21,WS-21.2417,Graph-to-Graph Transformer for Transition-based Dependency Parsing,Alireza Mohammadshahi|James Henderson,38940653 -WS-21,WS-21.26,Deeply Embedded Knowledge Representation Reasoning For Natural Language Question Answering: A Practitioner's Perspective,Arindam Mitra|Sanjay Narayana|Chitta Baral,38940152 -WS-21,WS-21.3373,An Empirical Investigation of Beam-Aware Training in Supertagging,Renato Negrinho|Matthew R. Gormley|Geoff Gordon,38940654 -WS-21,WS-21.4,CopyNext: Explicit Span Copying and Alignment in Sequence to Sequence Models,Abhinav Singh|Patrick Xia|Guanghui Qin|Mahsa Yarmohammadi|Benjamin Van Durme,38940142 -WS-21,WS-21.7,Generating Synthetic Data for Task-Oriented Semantic Parsing with Hierarchical Representations,Ke Tran|Ming Tan,38940155 -WS-21,WS-21.957,Multi^2OIE: Multilingual Open Information Extraction based on Multi-Head Attention with BERT,Youngbin Ro|Yukyung Lee|Pilsung Kang,38940647 -WS-21,WS-21.WS21-490,A Fully Hyperbolic Neural Model for Hierarchical Multi-Class Classification,Federico López|Michael Strube,38940646 -WS-23,WS-23.106,Towards End-to-End In-Image Neural Machine Translation,Elman Mansimov|Mitchell Stern|Mia Chen|Orhan Firat|Jakob Uszkoreit|Puneet Jain,38939782 -WS-23,WS-23.110,Modulated Fusion using Transformer for Linguistic-Acoustic Emotion Recognition,Jean-Benoit Delbrouck|Noé Tits|Stéphane Dupont,38939779 -WS-23,WS-23.114,Multimodal Speech Recognition with Unstructured Audio Masking,Tejas Srinivasan|Ramon Sanabria|Florian Metze|Desmond Elliott,38939780 -WS-23,WS-23.119,MAST: Multimodal Abstractive Summarization with Trimodal Hierarchical Attention,Aman Khullar|Udit Arora,38939781 -WS-23,WS-23.122,Reasoning Over History: Context Aware Visual Dialog,Muhammad Shah|Shikib Mehri|Tejas Srinivasan,38939783 -WS-23,WS-23.1589,No Gestures Left Behind: Learning Relationships between Spoken Language and Freeform Gestures,Chaitanya Ahuja Et Al,38940175 -WS-23,WS-23.3273,Beyond Language: Learning Commonsense from Images for Reasoning,Yanyan Lan Et Al,38940176 -WS-23,WS-23.Loic,A Vision on (Simultaneous) Multimodal Machine Translation,Loic Barrault,38939784 -WS-24,WS-24.1,On Log-Loss Scores and (No) Privacy,Abhinav Aggarwal|Zekun Xu|Oluwaseyi Feyisetan|Nathanael Teissier,38939769 -WS-24,WS-24.11,Surfacing Privacy Settings Using Semantic Matching,Rishabh Khandelwal|Asmit Nayak|Yao Yao|Kassem Fawaz,38939773 -WS-24,WS-24.12,Differentially Private Language Models Benefit from Public Pre-training,Gavin Kerrigan|Dylan Slack|Jens Tuyls,38939774 -WS-24,WS-24.2,A Differentially Private Text Perturbation Method Using Regularized Mahalanobis Metric,Zekun Xu|Abhinav Aggarwal|Oluwaseyi Feyisetan|Nathanael Teissier,38939770 -WS-24,WS-24.2534,A Semantics-based Approach to Disclosure Classification in User-Generated Online Content,Chandan Akiti,38940639 -WS-24,WS-24.3,TextHide: Tackling Data Privacy in Language Understanding Tasks,Yangsibo Huang|Zhao Song|Danqi Chen|Kai Li|Sanjeev Arora,38939771 -WS-24,WS-24.9,Identifying and Classifying Third-party Entities in Natural Language Privacy Policies,Mitra Bokaie Hosseini|Pragyan K C|Irwin Reyes|Serge Egelman,38939772 -WS-25,WS-25.33,The elephant in the interpretability room: Why use attention as explanation when we have saliency methods?,Jasmijn Bastings|Katja Filippova,38939764 -WS-25,WS-25.43,Dissecting Lottery Ticket Transformers: Structural and Behavioral Study of Sparse Neural Machine Translation,Rajiv Movva|Jason Zhao,38939765 -WS-25,WS-25.45,BERTs of a feather do not generalize together: Large variability in generalization across models with similar test set performance,R. Thomas Mccoy|Junghyun Min|Tal Linzen,38939766 -WS-25,WS-25.54,The EOS Decision and Length Extrapolation,Benjamin Newman|John Hewitt|Percy Liang|Christopher D. Manning,38939767 -WS-25,WS-25.59,Evaluating Attribution Methods using White-Box LSTMs,Yiding Hao,38939768 -WS-25,WS-25.70,diagNNose: A Library for Neural Activation Analysis,Jaap Jumelet,38940638 -WS-25,WS-25.8,What Happens To BERT Embeddings During Fine-tuning?,Amil Merchant|Elahe Rahimtoroghi|Ellie Pavlick|Ian Tenney,38939763 -WS-26,WS-26.1,COVID-19 Surveillance through Twitter using Self-Supervised Learning and Few Shot Learning,Brandon Lwowski|Peyman Najafirad,38939841 -WS-26,WS-26.10,Frugal neural reranking: evaluation on the Covid-19 literature,Tiago Almeida|Sérgio Matos,38939845 -WS-26,WS-26.16,CORA: A Deep Active Learning Covid-19 Relevancy Algorithm to Identify Core Scientific Articles,Zubair Afzal|Vikrant Yadav|Olga Fedorova|Vaishnavi Kandala|Janneke Van De Loo|Saber A Akhondi|Pascal Coupet|George Tsatsaronis,38939846 -WS-26,WS-26.2,A Multilingual Neural Machine Translation Model for Biomedical Data,Alexandre Berard|Zae Myung Kim|Vassilina Nikoulina|Eunjeong Lucy Park|Matthias Gallé,38939842 -WS-26,WS-26.20,Quantifying the Effects of COVID-19 on Mental Health Support Forums,Laura Biester|Katie Matton|Janarthanan Rajendran|Emily Mower|Rada Mihalcea,38939847 -WS-26,WS-26.28,Exploratory Analysis of COVID-19 Related Tweets in North America to Inform Public Health Institutes,Hyeju Jang|Emily Rempel|Giuseppe Carenini|Naveed Janjua,38939848 -WS-26,WS-26.31,Not Just Semantics: Social Distancing and COVID Discourse on Twitter,Austin Van Loon|Sheridan Stewart|Brandon Waldon|Shrinidhi K Lakshmikanth|Ishan Shah|Sharath Chandra Guntuku|Garrick Sherman|James Zou|Johannes Eichstaedt,38939849 -WS-26,WS-26.35,Expressive Interviewing: A Conversational System for Coping with COVID-19,Charles Welch|Allison Lahnala|Veronica Perez-Rosas|Siqi Shen|Sarah Seraj|Larry An|Kenneth Resnicow|James Pennebaker|Rada Mihalcea,38939850 -WS-26,WS-26.37,Detecting COVID-19 Misinformation on Social Media,Tamanna Hossain|Robert L. Logan Iv|Arjuna Ugarte|Yoshitomo Matsubara|Sean Young|Sameer Singh,38939851 -WS-26,WS-26.45,A System for Worldwide COVID-19 Information Aggregation,Akiko Aizawa|Frederic Bergeron|Junjie Chen|Fei Cheng|Katsuhiko Hayashi|Kentaro Inui|Hiroyoshi Ito|Daisuke Kawahara|Masaru Kitsuregawa|Hirokazu Kiyomaru|Masaki Kobayashi|Takashi Kodama|Sadao Kurohashi|Qianying Liu|Masaki Matsubara|Yusuke Miyao|Atsuyuki Morishima|Yugo Murawaki|Kazumasa Omura|Haiyue Song|Eiichiro Sumita|Shinji Suzuki|Ribeka Tanaka|Yu Tanaka|Masashi Toyoda|Nobuhiro Ueda|Honai Ueoka|Masao Utiyama|Ying Zhong,38939852 -WS-26,WS-26.47,Temporal Mental Health Dynamics on Social Media,Tom Tabak|Matthew Purver,38939853 -WS-26,WS-26.50,TICO-19: the Translation Initiative for COvid-19,Antonios Anastasopoulos|Alessandro Cattelan|Zi-Yi Dou|Marcello Federico|Christian Federmann|Dmitriy Genzel|Franscisco Guzmán|Junjie Hu|Macduff Hughes|Philipp Koehn|Rosie Lazar|Will Lewis|Graham Neubig|Mengmeng Niu|Alp Öktem|Eric Paquin|Grace Tang|Sylwia Tur,38939854 -WS-26,WS-26.53,Public Sentiment on Governmental COVID-19 Measures in Dutch Social Media,Shihan Wang|Marijn Schraagen|Erik Tjong Kim Sang|Mehdi Dastani,38939855 -WS-26,WS-26.6,Answering Questions on COVID-19 in Real-Time,Jinhyuk Lee|Sean S. Yi|Minbyul Jeong|Mujeen Sung|WonJin Yoon|Yonghwa Choi|Miyoung Ko|Jaewoo Kang,38939843 -WS-26,WS-26.60,COVID-19 Literature Topic-Based Search via Hierarchical NMF,Rachel Grotheer|Longxiu Huang|Yihuan Huang|Alona Kryshchenko|Oleksandr Kryshchenko|Pengyu Li|Xia Li|Elizaveta Rebrova|Kyung Ha|Deanna Needell,38939856 -WS-26,WS-26.63,Improved Topic Representations of Medical Documents to Assist COVID-19 Literature Exploration,Yulia Otmakhova|Karin Verspoor|Timothy Baldwin|Simon Suster,38939857 -WS-26,WS-26.64,Automatic Evaluation vs. User Preference in Neural Textual QuestionAnswering over COVID-19 Scientific Literature,Arantxa Otegi|Jon Ander Campos|Gorka Azkune|Aitor Soroa|Eneko Agirre,38939858 -WS-26,WS-26.65,Twitter Data Augmentation for Monitoring Public Opinion on COVID-19 Intervention Measures,Lin Miao|Mark Last|Marina Litvak,38939859 -WS-26,WS-26.9,CAiRE-COVID: A Question Answering and Query-focused Multi-Document Summarization System for COVID-19 Scholarly Information Management,Dan Su|Yan Xu|Tiezheng Yu|Farhad Bin Siddique|Elham Barezi|Pascale Fung,38939844 -WS-3,WS-3.1,Domain adaptation challenges of BERT in tokenization and sub-word representations of Out-of-Vocabulary words,Anmol Nayak|Hariprasad Timmapathini|Karthikeyan Ponnalagu|Vijendran Gopalan Venkoparao,38940788 -WS-3,WS-3.12,On Task-Level Dialogue Composition of Generative Transformer Model,Prasanna Parthasarathi|Sharan Narang|Arvind Neelakantan,38940793 -WS-3,WS-3.19,How Effectively Can Machines Defend Against Machine-Generated Fake News? An Empirical Study,Meghana Moorthy Bhat|Srinivasan Parthasarathy,38940794 -WS-3,WS-3.20,Label Propagation-Based Semi-Supervised Learning for Hate Speech Classification,Ashwin Geet D'Sa|Irina Illina|Dominique Fohr|Dietrich Klakow,38940795 -WS-3,WS-3.22,Layout-Aware Text Representations Harm Clustering Documents by Type,Catherine Finegan-Dollak|Ashish Verma,38940796 -WS-3,WS-3.23,An Analysis of Capsule Networks for Part of Speech Tagging in High- and Low-resource Scenarios,Andrew Zupon|Faiz Rafique|Mihai Surdeanu,38940797 -WS-3,WS-3.24,Can Knowledge Graph Embeddings Tell Us What Fact-checked Claims Are About?,Valentina Beretta|Sébastien Harispe|Katarina Boland|Luke Lo Seen|Konstantin Todorov|On Tchechmedjiev,38940798 -WS-3,WS-3.2436,WER we are and WER we think we are,Piotr Szymański|Piotr Żelasko|Mikolaj Morzy|Adrian Szymczak|Marzena Żyła-Hoppe|Joanna Banaszczak|Lukasz Augustyniak|Jan Mizgajski|Yishay Carmiel,38940634 -WS-3,WS-3.2575-ws3,What do we expect from Multiple-choice QA Systems?,Krunal Shah|Nitish Gupta|Dan Roth,38940809 -WS-3,WS-3.26,"Do Transformers Dream of Inference, or Can Pretrained Generative Models Learn Implicit Inferential Rules?",Zhengzhong Liang|Mihai Surdeanu,38940799 -WS-3,WS-3.27,Counterfactually-Augmented SNLI Training Data Does Not Yield Better Generalization Than Unaugmented Data,William Huang|Haokun Liu|Samuel R. Bowman,38940800 -WS-3,WS-3.29,NMF Ensembles? Not for Text Summarization!,Alka Khurana|Vasudha Bhatnagar,38940801 -WS-3,WS-3.30,"If You Build Your Own NER Scorer, Non-replicable Results Will Come",Constantine Lignos|Marjan Kamyab,38940802 -WS-3,WS-3.3004,Effects of Naturalistic Variation in Goal-Oriented Dialog,Jatin Ganhotra|Robert Moore|Sachindra Joshi|Kahini Wadhawan,38940807 -WS-3,WS-3.31,HINT3: Raising the bar for Intent Detection in the Wild,Gaurav Arora|Chirag Jain|Manas Chaturvedi|Krupal Modi,38940803 -WS-3,WS-3.3106,Poison Attacks against Text Datasets with Conditional Adversarially Regularized Autoencoder,Alvin Chan|Yi Tay|Yew-Soon Ong|Aston Zhang,38940808 -WS-3,WS-3.3296,How Effective is Task-Agnostic Data Augmentation for Pretrained Transformers?,Shayne Longpre|Yu Wang|Christopher DuBois,38940806 -WS-3,WS-3.33,The Extraordinary Failure of Complement Coercion Crowdsourcing,Yanai Elazar|Victoria Basmov|Shauli Ravfogel|Yoav Goldberg|Reut Tsarfat,38940804 -WS-3,WS-3.34,Embedding Structured Dictionary Entries,Steven Wilson|Walid Magdy|Barbara McGillivray|Gareth Tyson,38940805 -WS-3,WS-3.4,Q. Can Knowledge Graphs be used to Answer Boolean Questions? A. It's complicated!,Daria Dzendzik|Carl Vogel|Jennifer Foster,38940789 -WS-3,WS-3.6,How Far Can We Go with Data Selection? A Case Study on Semantic Sequence Tagging Tasks,Samuel Louvan|Bernardo Magnini,38940790 -WS-3,WS-3.7,Evaluating the Effectiveness of Efficient Neural Architecture Search for Sentence-Pair Tasks,Ansel MacLaughlin|Jwala Dhamala|Anoop Kumar|Sriram Venkatapathy|Ragav Venkatesan|Rahul Gupta,38940791 -WS-3,WS-3.8,Which Matters Most? Comparing the Impact of Concept and Document Relationships in Topic Models,Silvia Terragni|Debora Nozza|Elisabetta Fersini|Messina Enza,38940792 -WS-4,WS-4.1175,Filtering before Iteratively Referring for Knowledge-Grounded Response Selection in Retrieval-Based Chatbots,Jia-Chen Gu,38940705 -WS-4,WS-4.1735,TSDG: Content-aware Neural Response Generation with Two-stage Decoding Process,Junsheng Kong|Zhicheng Zhong|Yi Cai|Xin Wu|Da Ren,38940706 -WS-4,WS-4.1761-ws4,ConveRT: Efficient and Accurate Conversational Representations from Transformers,Ivan Vulić,38940707 -WS-4,WS-4.1947,Learning Improvised Chatbots from Adversarial Modifications of Natural Language Feedback,Makesh Narsimhan Sreedhar|Kun Ni|Siva Reddy,38940708 -WS-4,WS-4.2041,Toward Stance-based Personas for Opinionated Dialogues,Thomas Scialom|Serra Sinem Tekiroglu|Jacopo Staiano|Marco Guerini,38940704 -WS-4,WS-4.2889,Joint Turn and Dialogue level User Satisfaction Estimation on Multi-Domain Conversations,Praveen Kumar Bodigutla|Aditya Tiwari|Josep Vallas Vargas|Lazaros Polymenakos|Spyros Matsoukas,38940709 -WS-4,WS-4.2957,Making Information Seeking Easier: An Improved Pipeline for Conversational Search,Vaibhav Kumar|Jamie Callan,38940710 -WS-4,WS-4.3361,SMRT Chatbots: Improving Non-Task-Oriented Dialog with Simulated Multi-Reference Training,Huda Khayrallah|João Sedoc,38940711 -WS-4,WS-4.paper1,TBA,Tba,38940061 -WS-4,WS-4.paper2,A Wrong Answer or a Wrong Question? An Intricate Relationship between Question Reformulation and Answer Selection in Conversational Question Answering,Svitlana Vakulenko|Shayne Longpre|Zhucheng Tu|Raviteja Anantha,38940062 -WS-4,WS-4.paper3,Semantically Driven Sentence Fusion: Modeling and Evaluation,Eyal Ben-David,38940063 -WS-4,WS-4.paper4,TBA,Tba,38940064 -WS-4,WS-4.paper5,TBA,Marco Guerini,38940065 -WS-5,WS-5.16,Probabilistic Predictions of People Perusing: Evaluating Metrics of Language Model Performance for Psycholinguistic Modeling,Yiding Hao|Simon Mendelsohn|Rachel Sterneck|Randi Martinez|Robert Frank,38939682 -WS-5,WS-5.7,Images and Imagination: Automated Analysis of Priming Effects Related to Autism Spectrum Disorder and Developmental Language Disorder,Michaela Regneri|Diane King|Fahreen Walji|Olympia Palikara,38939683 -WS-5,WS-5.Keynote,How Languages Carve Up the World: Cognitive Explanation through Computational Modeling,Suzanne Stevenson,38939684 -WS-5,WS-5.Keynote2,"Information Processing, Communication, and Word Order",Richard Futrell,38939685 -WS-6,WS-6.10,Learning Adaptive Language Interfaces through Decomposition,Siddharth Karamcheti|Dorsa Sadigh|Percy Liang,38939456 -WS-6,WS-6.11,ColloQL: Robust Text-to-SQL Over Search Queries,Karthik Radhakrishnan|Arvind Srikantan|Xi Victoria Lin,38939457 -WS-6,WS-6.12,Natural Language Response Generation from SQL with Generalization and Back-translation,Saptarashmi Bandyopadhyay|Tianyang Zhao,38939458 -WS-6,WS-6.13,GRAPPA: Grammar-Augmented Pre-Training for Table Semantic Parsing,Tao Yu|Chien-Sheng Wu|Xi Victoria Lin|Bailin Wang|Yi Chern Tan|Xinyi Yang|Dragomir Radev|Richard Socher|Caiming Xiong,38939459 -WS-6,WS-6.14,Re-thinking Open-domain Semantic Parsing,Yu Gu|Sue Kase|Michelle Vanni|Brian Sadler|Percy Liang|Xifeng Yan|Yu Su,38939460 -WS-6,WS-6.15,Did You Ask a Good Question? A Cross-Domain Question Intention Classification Benchmark for Text-to-SQL,Yusen Zhang|Xiangyu Dong|Shuaichen Chang|Tao Yu|Peng Shi|Rui Zhang,38939461 -WS-6,WS-6.7,QA2Explanation: Generating and Evaluating Explanations for Question Answering Systems over Knowledge Graph,Saeedeh Shekarpour|Abhishek Nadgeri|Kuldeep Singh,38939453 -WS-6,WS-6.8,Uncertainty and Traffic-Aware Active Learning for Semantic Parsing,Priyanka Sen|Emine Yilmaz,38939454 -WS-6,WS-6.9,Improving Sequence-to-Sequence Semantic Parser for Task Oriented Dialog,Chaoting Xuan,38939455 -WS-7,WS-7.14,Improved Local Citation Recommendation Based on Context Enhanced with Global Information,Zoran Medić|Jan Snajder,38940720 -WS-7,WS-7.1457,Will this Idea Spread Beyond Academia? Understanding Knowledge Transfer of Scientific Concepts across Text Corpora,Hancheng Cao|Mengjie Cheng|Zhepeng Cen|Daniel A. McFarland|Xiang Ren,38940721 -WS-7,WS-7.15shared,"CIST@CL-SciSumm 2020, LongSumm 2020: Automatic Scientific Document Summarization",Lei Li|Yang Xie|Wei Liu|Yinan Liu|Yafei Jiang|Siya Qi|Xingyuan Li,38940743 -WS-7,WS-7.17,SciWING– A Software Toolkit for Scientific Document Processing,Abhinav Ramesh Kashyap|Min-Yen Kan,38940731 -WS-7,WS-7.1728,"MedICaT: A Dataset of Medical Images, Captions, and Textual References",Sanjay Subramanian|Lucy Lu Wang|Sachin Mehta|Ben Bogin|Madeleine Van Zuylen|Sravanthi Parasar|Sameer Singh|Matt Gardner|Hannaneh Hajishirzi,38940723 -WS-7,WS-7.18,NLP-PINGAN-TECH @ CL-SciSumm 2020,Ling Chai|Guizhen Fu|Yuan Ni,38941223 -WS-7,WS-7.20shared,Dimsum @LaySumm 20,Tiezheng Yu|Dan Su|Wenliang Dai|Pascale Fung,38940741 -WS-7,WS-7.21,Multi-task Peer-Review Score Prediction,Jiyi Li|Ayaka Sato|Kazuya Shimura|Fumiyo Fukumoto,38940727 -WS-7,WS-7.22,ERLKG: Entity Representation Learning and Knowledge Graph based association analysis of COVID-19 through mining of unstructured biomedical corpora,Sayantan Basu|Sinchani Chakraborty|Atif Hassan|Sana Siddique|Ashish Anand,38940725 -WS-7,WS-7.24,Towards Grounding of Formulae,Takuto Asakura|ré Greiner-Petter|Akiko Aizawa|Yusuke Miyao,38940733 -WS-7,WS-7.2502,Literature Retrieval for Precision Medicine with Neural Matching and Faceted Summarization,Jiho Noh|Ramakanth Kavuluru,38940722 -WS-7,WS-7.26,Learning CNF Blocking for Large-scale Author Name Disambiguation,Kunho Kim|Athar Sefid|C Lee Giles,38940717 -WS-7,WS-7.27,SChuBERT: Scholarly Document Chunks with BERT-encoding boost Citation Count Prediction,Thomas Van Dongen|Gideon Maillette De Buy Wenniger|Lambert Schomaker,38940730 -WS-7,WS-7.29,Structure-Tags Improve Text Classification for Scholarly Document Quality Prediction,Gideon Maillette De Buy Wenniger|Thomas Van Dongen|Eleri Aedmaa|Herbert Teun Kruitbosch|Edwin A. Valentijn|Lambert Schomaker,38940732 -WS-7,WS-7.30,Cydex: Neural Search Infrastructure for the Scholarly Literature,Shane Ding|Edwin Zhang|Jimmy Lin,38940734 -WS-7,WS-7.32,A Smart System to Generate and Validate Question Answer Pairs for COVID-19 Literature,Rohan Bhambhoria|Luna Feng|Dawn Sepehr|John Chen|Conner Cowling|Sedef Kocak|Elham Dolatabadi,38940713 -WS-7,WS-7.35,On the Use of Web Search to Improve Scientific Collections,Krutarth Patel|Cornelia Caragea|Sujatha Das Gollapalli,38940728 -WS-7,WS-7.36,Scaling Systematic Literature Reviews with Machine Learning Pipelines,Seraphina Goldfarb-Tarrant|Alexander Robertson|Louise Donnison|Jasmina Lazic|Theodora Tsouloufi|Karen Smyth,38940729 -WS-7,WS-7.37shared,Using Pre-Trained Transformer for a better Lay Summarization,Seungwon Kim,38940740 -WS-7,WS-7.39,Acknowledgement Entity Recognition in CORD-19 Papers,Jian Wu|Pei Wang|Xin Wei|Sarah Rajtmajer|C Lee Giles|Christopher Griffin,38940712 -WS-7,WS-7.40,DeepPaperComposer: A Simple Solution for Training Data Preparation for Parsing Research Papers,Meng Ling|Jian Chen,38940719 -WS-7,WS-7.42,"Document-Level Definition Detection in Scholarly Documents: Existing Models, Error Analyses, and Future Directions",Dongyeop Kang|Rew Head|Risham Sidhu|Kyle Lo|Daniel Weld|Marti A. Hearst,38940724 -WS-7,WS-7.43shared,"IIITBH-IITP@CL-SciSumm20, CL-LaySumm20, LongSumm20",Saichethan Miriyala Reddy|Naveen Saini|Sriparna Saha|Pushpak Bhattacharyya,38940739 -WS-7,WS-7.44,Reconstructing Manual Information Extraction with DB-to-Document Backprojection: Experiments in the Life Science Domain,Mark-Christoph Muller|Sucheta Ghosh|Maja Rey|Ulrike Wittig|Wolfgang Muller|Michael Strube,38940718 -WS-7,WS-7.45,"AUTH @ CLSciSumm 20, LaySumm 20, LongSumm 20",Alexios Gidiotis|Stefanos Stefanidis|Grigorios Tsoumakas,38941222 -WS-7,WS-7.48_2shared,"SUMMAFORMERS @ LAYSUMM 20, LONGSUMM 20",Sayar Ghosh Roy|Nikhil Pinnaparaju|Risubh Jain|Manish Gupta|Vasudeva Varma,38940742 -WS-7,WS-7.48shared,Scientific Document Summarization for LaySumm '20 and LongSumm '2,Sayar Ghosh Roy|Nikhil Pinnaparaju|Risubh Jain|Manish Gupta∗|Vasudeva Varma,38940738 -WS-7,WS-7.49,UniHD@CL-SciSumm 2020: Citation Extraction as Search,Dennis Aumiller|Satya Almasian|Philip Hausner|Michael Gertz,38941224 -WS-7,WS-7.5,"On the effectiveness of small, discriminatively pre-trained language representation models for biomedical text mining",Ibrahim Burak Ozyurt,38940735 -WS-7,WS-7.54,CMU: CiteQA@CLSciSumm 2020,Anjana Umapathy|Karthik Radhakrishnan|Kinjal Jain|Rahul Singh,38941225 -WS-7,WS-7.56shared,GUIR @ LongSumm 2020: Learning to Generate Long Summaries from Scientific Documents,Sajad Sotudeh Gharebagh|Arman Cohan|Nazli Goharian,38940737 -WS-7,WS-7.6,The impact of preprint servers in the formation of novel ideas,Swarup Satish|Zonghai Yao|Rew Drozdov|Boris Veytsman,38940715 -WS-7,WS-7.60,Covidex: Neural Ranking Models and Keyword Search Infrastructure for the COVID-19 Open Research Dataset.,Edwin Zhang|Nikhil Gupta|Raphael Tang|Xiao Han|Ronak Pradeep|Kuang Lu|Yue Zhang|Rodrigo Nogueira|Kyunghyun Cho|Hui Fang|Jimmy Lin,38940714 -WS-7,WS-7.61,A New Neural Search and Insights Platform for Navigating and Organizing AI Research,Marzieh Fadaee|Olga Gureenkova|Fernando Rejon Barrera|Carsten Schnober|Wouter Weerkamp|Jakub Zavrel,38940726 -WS-7,WS-7.758,Where to Submit? Helping Researchers to Choose the Right Venue,Konstantin Kobs|Tobias Koopmann|Albin Zehe|David Fernes|Philipp Krop|Reas Hotho,38940736 -WS-7,WS-7.8,Effective Distributed Representations for Academic Expert Search,Mark Berger|Jakub Zavrel|Paul Groth,38940716 -WS-9,WS-9.10,"fugashi, a Tool for Tokenizing Japanese in Python",Paul McCann,38939744 -WS-9,WS-9.11,Going Beyond T-SNE: Exposing whatlies in Text Embeddings,Vincent Warmerdam|Thomas Kober|Rachael Tatman,38939745 -WS-9,WS-9.12,"Howl: A Deployed, Open-Source Wake Word Detection System",Raphael Tang|Jaejun Lee|Afsaneh Razi|Julia Cambre|Ian Bicking|Jofish Kaye|Jimmy Lin,38939746 -WS-9,WS-9.13,iNLTK: Natural Language Toolkit for Indic Languages,Gaurav Arora,38939747 -WS-9,WS-9.14,iobes: A Library for Span-Level Processing,Brian Lester,38939748 -WS-9,WS-9.15,jiant: A Software Toolkit for Research on General-Purpose Text Understanding Models,Yada Pruksachatkun|Phil Yeres|Haokun Liu|Jason Phang|Phu Mon Htut|Alex Wang|Ian Tenney|Samuel R. Bowman,38939749 -WS-9,WS-9.16,KLPT – Kurdish Language Processing Toolkit,Sina Ahmadi,38939750 -WS-9,WS-9.17,Open Korean Corpora: A Practical Report,Won Ik Cho|Sangwhan Moon|Youngsook Song,38939751 -WS-9,WS-9.18,Open-Source Morphology for Endangered Mordvinic Languages,Jack Rueter|Mika Hämäläinen|Niko Partanen,38939752 -WS-9,WS-9.19,Pimlico: A toolkit for corpus-processing pipelines and reproducible experiments,Mark Granroth-Wilding,38939753 -WS-9,WS-9.20,PySBD: Pragmatic Sentence Boundary Disambiguation,Nipun Sadvilkar|Mark Neumann,38939754 -WS-9,WS-9.21,SacreROUGE: An Open-Source Library for Using and Developing Summarization Evaluation Metrics,Daniel Deutsch|Dan Roth,38939755 -WS-9,WS-9.22,TextAttack: Lessons learned in designing Python frameworks for NLP,John Morris|Jin Yong Yoo|Yanjun Qi,38939756 -WS-9,WS-9.23,"TOMODAPI: A Topic Modeling API to Train, Use and Compare Topic Models",Pasquale Lisena|Ismail Harrando|Oussama Kandakji|Raphael Troncy,38939757 -WS-9,WS-9.24,User-centered Robust NLP OSS: Lessons Learned from Developing Maintaining RSMTool,Nitin Madnani|Anastassia Loukina,38939758 -WS-9,WS-9.25,WAFFLE: A Graph for WordNet Applied to FreeForm Linguistic Exploration,Berk Ekmekci|Blake Howald,38939759 -WS-9,WS-9.4,A Framework to Assist Chat Operators of Mental Healthcare Services,Thiago Madeira|Heder Bernardino|Jairo Francisco De Souza|Henrique Gomide|Nathália Munck Machado|Bruno Marcos Pinheiro Da Silva|Alexandre Vieira Pereira Pacelli,38939738 -WS-9,WS-9.5,ARBML: Democritizing Arabic Natural Language Processing Tools,Zaid Alyafeai|Maged Al-Shaibani,38939739 -WS-9,WS-9.6,CLEVR Parser: A Graph Parser Library for Geometric Learning on Language Grounded Image Scenes,Raeid Saqur|Ameet Deshpande,38939740 -WS-9,WS-9.7,End-to-end NLP Pipelines in Rust,Guillaume Becquin,38939741 -WS-9,WS-9.8,Fair Embedding Engine: A Library for Analyzing and Mitigating Gender Bias in Word Embeddings,Vaibhav Kumar|Tenzin Bhotia|Vaibhav Kumar,38939742 -WS-9,WS-9.9,Flexible retrieval with NMSLIB and FlexNeuART,Leonid Boytsov|Eric Nyberg,38939743 + with Graph Memory Networks",Hongyin Luo|Shang-Wen Li|James Glass,,38939821,https://www.aclweb.org/anthology/2020.clinicalnlp-1.16 +WS-12,WS-12.20,Pretrained Language Models for Biomedical and Clinical Tasks: Understanding and Extending the State-of-the-Art,Patrick Lewis|Myle Ott|Jingfei Du|Veselin Stoyanov,,38939822,https://www.aclweb.org/anthology/2020.clinicalnlp-1.17 +WS-12,WS-12.21,Incorporating Risk Factor Embeddings in Pre-trained Transformers Improves Sentiment Prediction in Psychiatric Discharge Summaries,Xiyu Ding|Mei-Hua Hall|Timothy Miller,,38939823,https://www.aclweb.org/anthology/2020.clinicalnlp-1.4 +WS-12,WS-12.2127,PharmMT: A Neural Machine Translation Approach to Simplify Prescription Directions,Jiazhao Li|Corey Lester|Xinyan Zhao|Yuting Ding|Yun Jiang|V.G.Vinod Vydiswaran,,38940180,https://www.aclweb.org/anthology/2020.findings-emnlp.251 +WS-12,WS-12.23,Assessment of DistilBERT performance on Named Entity Recognition task for the detection of Protected Health Information and medical concepts,Macarious Abadeer,,38939824,https://www.aclweb.org/anthology/2020.clinicalnlp-1.18 +WS-12,WS-12.25,Distinguishing between Dementia with Lewy bodies (DLB) and Alzheimer's Disease (AD) using Mental Health Records: a Classification Approach,Zixu Wang|Julia Ive|Sinead Moylett|Christoph Mueller|Rudolf Cardinal|Sumithra Velupillai|John O'Brien|Robert Stewart,,38939825, +WS-12,WS-12.2509,A Dual-Attention Network for Joint Named Entity Recognition and Sentence Classification of Adverse Drug Events,Susmitha Wunnava|Xiao Qin|Tabassum Kakar|Xiangnan Kong|Elke A. Rundensteiner,,38940181,https://www.aclweb.org/anthology/2020.findings-emnlp.306 +WS-12,WS-12.26,Weakly Supervised Medication Regimen Extraction from Medical Conversations,Dhruvesh Patel|Sandeep Konam|Sai Prabhakar,,38939826,https://www.aclweb.org/anthology/2020.clinicalnlp-1.20 +WS-12,WS-12.27,Extracting Relations between Radiotherapy Treatment Details,Danielle Bitterman|Timothy Miller|David Harris|Chen Lin|Sean Finan|Jeremy Warner|Raymond Mak|Guergana Savova,,38939827,https://www.aclweb.org/anthology/2020.clinicalnlp-1.21 +WS-12,WS-12.2801,Dr. Summarize: Global Summarization of Medical Dialogue by Exploiting Local Structures,Anirudh Joshi|Namit Katariya|Xavier Amatriain|Anitha Kannan,,38940182,https://www.aclweb.org/anthology/2020.findings-emnlp.335 +WS-12,WS-12.2804,Generating Accurate Electronic Health Assessment from Medical Graph,Zhichao Yang|Hong Yu,,38940183,https://www.aclweb.org/anthology/2020.findings-emnlp.336 +WS-12,WS-12.29,The Chilean Waiting List Corpus: a new resource for clinical Named Entity Recognition in Spanish,Pablo Báez|Fabián Villena|Matías Rojas|Manuel Durán|Jocelyn Dunstan,,38939828,https://www.aclweb.org/anthology/2020.clinicalnlp-1.32 +WS-12,WS-12.3,Classification of Syncope Cases in Norwegian Medical Records,Ildiko Pilan|Pål H. Brekke|Fredrik A. Dahl|Tore Gundersen|Haldor Husby|Øystein Nytrø|Lilja Øvrelid,,38939808,https://www.aclweb.org/anthology/2020.clinicalnlp-1.9 +WS-12,WS-12.30,BioBERTpt - A Portuguese Neural Language Model for Clinical Named Entity Recognition,Elisa Terumi Rubel Schneider|João Vitor Andrioli De Souza|Julien Knafou|Lucas Emanuel Silva E Oliveira|Jenny Copara|Yohan Bonescki Gumiel|Lucas Ferro Antunes De Oliveira|Emerson Cabrera Paraiso|Douglas Teodoro|Cláudia Maria Cabral Moro Barra,,38939829,https://www.aclweb.org/anthology/2020.clinicalnlp-1.7 +WS-12,WS-12.31,Cancer Registry Information Extraction via Transfer Learning,Yan-Jie Lin|Hong-Jie Dai|You-Chen Zhang|Chung-Yang Wu|Yu-Cheng Chang|Pin-Jou Lu|Chih-Jen Huang|Yu-Tsang Wang|Hui-Min Hsieh|Kun-San Chao|Tsang-Wu Liu|I-Shou Chang|Yi-Hsin Connie Yang|Ti-Hao Wang|Ko-Jiunn Liu|Li-Tzong Chen|Sheau-Fang Yang,,38939830,https://www.aclweb.org/anthology/2020.clinicalnlp-1.22 +WS-12,WS-12.33,Utilizing Multimodal Feature Consistency to Detect Adversarial Examples on Clinical Summaries,Wenjie Wang|Youngja Park|Taesung Lee|Ian Molloy|Pengfei Tang|Li Xiong,,38939831,https://www.aclweb.org/anthology/2020.clinicalnlp-1.29 +WS-12,WS-12.37,PHICON: Improving Generalization of Clinical Text De-identification Models via Data Augmentation,Xiang Yue|Shuang Zhou,,38939832,https://www.aclweb.org/anthology/2020.clinicalnlp-1.23 +WS-12,WS-12.38,Where's the Question? A Multi-channel Deep Convolutional Neural Network for Question Identification in Textual Data,George Michalopoulos|Helen Chen|Alexander Wong,,38939833,https://www.aclweb.org/anthology/2020.clinicalnlp-1.24 +WS-12,WS-12.39,Advancing Seq2seq with Joint Paraphrase Learning,So Yeon Min|Preethi Raghavan|Peter Szolovits,,38939834,https://www.aclweb.org/anthology/2020.clinicalnlp-1.30 +WS-12,WS-12.4,Comparison of Machine Learning Methods for Multi-label Classificationof Nursing Education and Licensure Exam Questions,John Langton|Krishna Srihasam|Junlin Jiang,,38939809,https://www.aclweb.org/anthology/2020.clinicalnlp-1.10 +WS-12,WS-12.40,Learning from Unlabelled Data for Clinical Semantic Textual Similarity,Yuxia Wang|Karin Verspoor|Timothy Baldwin,,38939835,https://www.aclweb.org/anthology/2020.clinicalnlp-1.25 +WS-12,WS-12.43,BERT-XML: Large Scale Automated ICD Coding Using BERTPretraining,Zachariah Zhang|Jingshu Liu|Narges Razavian,,38939836,https://www.aclweb.org/anthology/2020.clinicalnlp-1.3 +WS-12,WS-12.44,Joint Learning with Pre-trained Transformer on Named Entity Recognition and Relation Extraction Tasks for Clinical Analytics,Miao Chen|Ganhui Lan|Fang Du|Victor Lobanov,,38939837,https://www.aclweb.org/anthology/2020.clinicalnlp-1.26 +WS-12,WS-12.48,Analyzing Text Specific vs Blackbox Fairness Algorithms in Multimodal Clinical NLP,John Chen|Ian Berlot-Attwell|Xindi Wang|Safwan Hossain|Frank Rudzicz,"Clinical machine learning is increasingly multimodal, collected in both structured tabular formats and unstructured forms such as free text. We propose a novel task of exploring fairness on a multimodal clinical dataset, adopting equalized odds for the downstream medical prediction tasks. To this end, we investigate a modality-agnostic fairness algorithm - equalized odds post processing - and compare it to a text-specific fairness algorithm: debiased clinical word embeddings. Despite the fact that debiased word embeddings do not explicitly address equalized odds of protected groups, we show that a text-specific approach to fairness may simultaneously achieve a good balance of performance classical notions of fairness. Our work opens the door for future work at the critical intersection of clinical NLP and fairness.",38939838,https://www.aclweb.org/anthology/2020.clinicalnlp-1.33 +WS-12,WS-12.49,Extracting Semantic Aspects for Structured Representation of Clinical Trial Eligibility Criteria,Tirthankar Dasgupta|Ishani Mondal|Abir Naskar|Lipika Dey,,38939839,https://www.aclweb.org/anthology/2020.clinicalnlp-1.27 +WS-12,WS-12.6,Clinical XLNet: Modeling Sequential Clinical Notes and Predicting Prolonged Mechanical Ventilation,Kexin Huang|Abhishek Singh|Sitong Chen|Edward Moseley|Chih-Ying Deng|Naomi George|Charolotta Lindvall,,38939810,https://www.aclweb.org/anthology/2020.clinicalnlp-1.11 +WS-12,WS-12.7,Automatic recognition of abdominal lymph nodes from clinical text,Yifan Peng|Sungwon Lee|Daniel C. Elton|Thomas Shen|Yu-xing Tang|Qingyu Chen|Shuai Wang|Yingying Zhu|Ronald Summers|Zhiyong Lu,,38939811,https://www.aclweb.org/anthology/2020.clinicalnlp-1.12 +WS-12,WS-12.8,On the diminishing return of labeling clinical reports,Jean-Baptiste Lamare|Oloruntobiloba Olatunji|Li Yao,,38939812,https://www.aclweb.org/anthology/2020.clinicalnlp-1.31 +WS-13,WS-13.1,Correcting the Misuse: A Method for the Chinese Idiom Cloze Test,Xinyu Wang|Hongsheng Zhao|Tan Yang|Hongbo Wang,,38939724,https://www.aclweb.org/anthology/2020.deelio-1.1 +WS-13,WS-13.1008,AutoETER: Automated Entity Type Representation for Knowledge Graph Embedding,Yongfei Zhang,,38940167,https://www.aclweb.org/anthology/2020.findings-emnlp.105 +WS-13,WS-13.1039,Dynamic Semantic Matching and Aggregation Network for Few-shot Intent Detection,Hoang Nguyen,,38940168,https://www.aclweb.org/anthology/2020.findings-emnlp.108 +WS-13,WS-13.1059,Biomedical Event Extraction on Graph Edge-conditioned Attention Networks with Hierarchical Knowledge Graphs,Kung-Hsiang Huang,,38940169, +WS-13,WS-13.12,Entity Attribute Relation Extraction with Attribute-Aware Embeddings,Dan Iter|Xiao Yu|Fangtao Li,,38939729,https://www.aclweb.org/anthology/2020.deelio-1.6 +WS-13,WS-13.1524,Continual Learning Long Short Term Memory,Xiaolong Wang,,38940170,https://www.aclweb.org/anthology/2020.findings-emnlp.164 +WS-13,WS-13.16,Enhancing Question Answering by Injecting Ontological Knowledgethrough Regularization,Travis Goodwin|Dina Demner-Fushman,,38939730,https://www.aclweb.org/anthology/2020.deelio-1.7 +WS-13,WS-13.17,Target Concept Guided Medical Concept Normalization in Noisy User-Generated Texts,Katikapalli Subramanyam Kalyan|Sivanesan Sangeetha,,38939731,https://www.aclweb.org/anthology/2020.deelio-1.8 +WS-13,WS-13.18,Incorporating Commonsense Knowledge Graph in Pretrained Models for Social Commonsense Tasks,Ting-Yun Chang|Yang Liu|Karthik Gopalakrishnan|Behnam Hedayatnia|Pei Zhou|Dilek Hakkani-Tur,,38939732,https://www.aclweb.org/anthology/2020.deelio-1.9 +WS-13,WS-13.20,Commonsense Statements Identification and Explanation with Transformer based Encoders,Sonia Cibu|Anca Marginean,,38939733,https://www.aclweb.org/anthology/2020.deelio-1.10 +WS-13,WS-13.2047,Out-of-Sample Representation Learning for Knowledge Graphs,Marjan Albooyeh,,38940171,https://www.aclweb.org/anthology/2020.findings-emnlp.241 +WS-13,WS-13.2085,Visual Objects As Context: Exploiting Visual Objects for Lexical Entailment,Masayasu Muraoka,"We propose a new word representation method derived from visual objects in associated images to tackle the lexical entailment task. Although it has been shown that the Distributional Informativeness Hypothesis (DIH) holds on text, in which the DIH assumes that a context surrounding a hyponym is more informative than that of a hypernym, it has never been tested on visual objects. Since our perception is tightly associated with language, it is meaningful to explore whether the DIH holds on visual objects. To this end, we consider visual objects as the context of a word and represent a word as a bag of visual objects found in images associated with the word. This allows us to test the feasibility of the visual DIH. To better distinguish word pairs in a hypernym relation from other relations such as co-hypernyms, we also propose a new measurable function that takes into account both the difference in the generality of meaning and similarity of meaning between words. Our experimental results show that the DIH holds on visual objects and that the proposed method combined with the proposed function outperforms existing unsupervised representation methods.",38940172,https://www.aclweb.org/anthology/2020.findings-emnlp.246 +WS-13,WS-13.22,"On the Complementary Nature of Knowledge Graph Embedding, Fine Grain Entity Types, and Language Modeling",Rajat Patel|Francis Ferraro,,38939734,https://www.aclweb.org/anthology/2020.deelio-1.11 +WS-13,WS-13.2513,BERT-kNN: Adding a kNN Search Component to Pretrained Language Models for Better QA,Nora Kassner,,38940173,https://www.aclweb.org/anthology/2020.findings-emnlp.307 +WS-13,WS-13.2666,Cross-Lingual Text Classification with Minimal Resources by Transferring a Sparse Teacher,Giannis Karamanolakis,,38940174,https://www.aclweb.org/anthology/2020.findings-emnlp.323 +WS-13,WS-13.4,Relation Extraction with Contextualized Relation Embedding,Xiaoyu Chen|Rohan Badlani,,38939725,https://www.aclweb.org/anthology/2020.deelio-1.2 +WS-13,WS-13.6,Generalization to Mitigate Synonym Substitution Attacks,Basemah Alshemali|Jugal Kalita,,38939726,https://www.aclweb.org/anthology/2020.deelio-1.3 +WS-13,WS-13.696,E-BERT: Efficient-Yet-Effective Entity Embeddings for BERT,Nina Poerner,,38940166,https://www.aclweb.org/anthology/2020.findings-emnlp.71 +WS-13,WS-13.7,Data Augmentation for Finetuning Text Generators,Steven Y. Feng|Varun Gangal|Dongyeop Kang|Teruko Mitamura|Eduard Hovy,,38939727,https://www.aclweb.org/anthology/2020.deelio-1.4 +WS-13,WS-13.9,Common Sense or World Knowledge? Investigating Adapter-Based Knowledge Injection into Pretrained Transformers,Anne Lauscher|Olga Majewska|Leonardo F. R. Ribeiro|Iryna Gurevych|Nikolai Rozanov|Goran Glavaš,,38939728,https://www.aclweb.org/anthology/2020.deelio-1.5 +WS-15,WS-15.1,Knowing Right from Wrong: Should We Use More Complex Models for Automatic Short-Answer Scoring in Bahasa Indonesia?,Ali Akbar Septiandri|Yosef Ardhito Winatmoko|Ilham Firdausi Putra,,38939419,https://www.aclweb.org/anthology/2020.sustainlp-1.1 +WS-15,WS-15.1098,OptSLA: an Optimization-Based Approach for Sequential Label Aggregation,Qi Li,,38940107,https://www.aclweb.org/anthology/2020.findings-emnlp.119 +WS-15,WS-15.12,Exploring the Boundaries of Low-Resource BERT Distillation,Moshe Wasserblat|Oren Pereg|Peter Izsak,,38939426,https://www.aclweb.org/anthology/2020.sustainlp-1.5 +WS-15,WS-15.1286,Inexpensive Domain Adaptation of Pretrained Language Models: Case Studies on Biomedical NER and Covid-19 QA,Nina Poerner,,38940121,https://www.aclweb.org/anthology/2020.findings-emnlp.134 +WS-15,WS-15.13,Efficient Estimation of Influence of a Training Instance,Sosuke Kobayashi|Sho Yokoi|Jun Suzuki|Kentaro Inui,,38939427,https://www.aclweb.org/anthology/2020.sustainlp-1.6 +WS-15,WS-15.14,Efficient Inference For Neural Machine Translation,Yi-Te Hsu|Sarthak Garg|Yi-Hsiu Liao|Ilya Chatsviorkin,,38939429,https://www.aclweb.org/anthology/2020.sustainlp-1.7 +WS-15,WS-15.1418,TopicBERT for Energy Efficient Document Classification,Yatin Chaudhary,,38940122,https://www.aclweb.org/anthology/2020.findings-emnlp.152 +WS-15,WS-15.1537,Constrained Decoding for Computationally Efficient Named Entity Recognition Taggers,Brian Lester,,38940105,https://www.aclweb.org/anthology/2020.findings-emnlp.166 +WS-15,WS-15.17,Sparse Optimization for Unsupervised Extractive Summarization of Long Documents with the Frank-Wolfe Algorithm,Alicia Tsai|Laurent El Ghaoui,,38939430,https://www.aclweb.org/anthology/2020.sustainlp-1.8 +WS-15,WS-15.1887,Semi-supervised Formality Style Transfer using LanguageModel Discriminator and Mutual Information Maximization,Kunal Chawla,,38940140, +WS-15,WS-15.2,Rank and run-time aware compression of NLP Applications,Urmish Thakker|Jesse Beu|Dibakar Gope|Ganesh Dasika|Matthew Mattina,,38939420,https://www.aclweb.org/anthology/2020.sustainlp-1.2 +WS-15,WS-15.2015,Blockwise Self-Attention for Long Document Understanding,Jiezhong Qiu,,38940119,https://www.aclweb.org/anthology/2020.findings-emnlp.232 +WS-15,WS-15.2017,Unsupervised Few-Bits Semantic Hashing with Implicit Topics Modeling,Fanghua Ye,,38940106,https://www.aclweb.org/anthology/2020.findings-emnlp.233 +WS-15,WS-15.2182,DiPair: Fast and Accurate Distillation for Trillion-ScaleText Matching and Pair Modeling,Jiecao Chen,,38940104, +WS-15,WS-15.22,Don't Read Too Much Into It: Adaptive Computation for Open-Domain Question Answering,Yuxiang Wu|Pasquale Minervini|Pontus Stenetorp|Sebastian Riedel,,38939431,https://www.aclweb.org/anthology/2020.sustainlp-1.9 +WS-15,WS-15.2230,General Purpose Text Embeddings from Pre-trained Language Models for Scalable Inference,Myle Ott,,38940109,https://www.aclweb.org/anthology/2020.findings-emnlp.271 +WS-15,WS-15.2288,Domain Adversarial Fine-Tuning as an Effective Regularizer,Giorgos Vernikos,,38940129,https://www.aclweb.org/anthology/2020.findings-emnlp.278 +WS-15,WS-15.2453,Improve Transformer Models with Better Relative Position Embeddings,Zhiheng Huang,,38940108,https://www.aclweb.org/anthology/2020.findings-emnlp.298 +WS-15,WS-15.2516,Identifying Spurious Correlations for Robust Text Classification,Zhao Wang,,38940117,https://www.aclweb.org/anthology/2020.findings-emnlp.308 +WS-15,WS-15.27,Doped Structured Matrices for Extreme Compression of LSTM Models,Urmish Thakker,,38940744, +WS-15,WS-15.28,A Two-stage Model for Slot Filling in Low-resource Settings: Domain-agnostic Non-slot Reduction and Pretrained Contextual Embeddings,Cennet Oguz|Ngoc Thang Vu,,38939432,https://www.aclweb.org/anthology/2020.sustainlp-1.10 +WS-15,WS-15.29,Early Exiting BERT for Efficient Document Ranking,Ji Xin|Rodrigo Nogueira|Yaoliang Yu|Jimmy Lin,,38939433,https://www.aclweb.org/anthology/2020.sustainlp-1.11 +WS-15,WS-15.3,Incremental Neural Coreference Resolution in Constant Memory,Patrick Xia|João Sedoc|Benjamin Van Durme,,38939421, +WS-15,WS-15.30,Keyphrase Generation with GANs in Low-Resources Scenarios,Giuseppe Lancioni|Saida S.Mohamed|Beatrice Portelli|Giuseppe Serra|Carlo Tasso,,38939434,https://www.aclweb.org/anthology/2020.sustainlp-1.12 +WS-15,WS-15.3078,SupMMD: A Sentence Importance Model for Extractive Summarization using Maximum Mean Discrepancy,Umanga Bista,,38940131,https://www.aclweb.org/anthology/2020.findings-emnlp.367 +WS-15,WS-15.32,Quasi-Multitask Learning: an Efficient Surrogate for Obtaining Model Ensembles,Norbert Kis-Szabó|Gábor Berend,,38939435,https://www.aclweb.org/anthology/2020.sustainlp-1.13 +WS-15,WS-15.34,A Little Bit Is Worse Than None: Ranking with Limited Training Data,Xinyu Zhang|Andrew Yates|Jimmy Lin,,38939436,https://www.aclweb.org/anthology/2020.sustainlp-1.14 +WS-15,WS-15.3444,Multi-hop Question Generation with Graph Convolutional Network,Dan Su,,38940120,https://www.aclweb.org/anthology/2020.findings-emnlp.416 +WS-15,WS-15.3459,Guiding Attention for Self-Supervised Learning with Transformers,Ameet Deshpande,,38940124,https://www.aclweb.org/anthology/2020.findings-emnlp.419 +WS-15,WS-15.3526,Probabilstic Case-based Reasoning for Open-World Knowledge Graph Completion,Rajarshi Das,,38940133, +WS-15,WS-15.3562,Extremely Low Bit Transformer Quantization for On-Device Neural Machine Translation,Insoo Chung,,38940118,https://www.aclweb.org/anthology/2020.findings-emnlp.433 +WS-15,WS-15.36,Predictive Model Selection for Transfer Learning in Sequence Labeling Tasks,Parul Awasthy|Bishwaranjan Bhattacharjee|John Kender|Radu Florian,,38939437,https://www.aclweb.org/anthology/2020.sustainlp-1.15 +WS-15,WS-15.361,Understanding tables with intermediate pre-training,Julian Martin Eisenschlos,,38940134,https://www.aclweb.org/anthology/2020.findings-emnlp.27 +WS-15,WS-15.37,Load What You Need: Smaller Versions of Mutlilingual BERT,Amine Abdaoui|Camille Pradel|Grégoire Sigel,,38939438,https://www.aclweb.org/anthology/2020.sustainlp-1.16 +WS-15,WS-15.38,SqueezeBERT: What can computer vision teach NLP about efficient neural networks?,Forrest Iandola|Albert Shaw|Ravi Krishna|Kurt Keutzer,,38939439,https://www.aclweb.org/anthology/2020.sustainlp-1.17 +WS-15,WS-15.381,Enhance Robustness of Sequence Labelling with Masked Adversarial Training,Luoxin Chen,,38940127,https://www.aclweb.org/anthology/2020.findings-emnlp.28 +WS-15,WS-15.39,Analysis of Resource-efficient Predictive Models for Natural Language Processing,Raj Pranesh|Ambesh Shekhar,,38939440,https://www.aclweb.org/anthology/2020.sustainlp-1.18 +WS-15,WS-15.42,Towards Accurate and Reliable Energy Measurement of NLP Models,Qingqing Cao|Aruna Balasubramanian|Niranjan Balasubramanian,,38939441,https://www.aclweb.org/anthology/2020.sustainlp-1.19 +WS-15,WS-15.43,FastFormers: Highly Efficient Transformer Models for Natural Language Understanding,Young Jin Kim|Hany Hassan,,38939442,https://www.aclweb.org/anthology/2020.sustainlp-1.20 +WS-15,WS-15.45,A comparison between CNNs and WFAs for Sequence Classification,Ariadna Quattoni|Xavier Carreras,,38939443,https://www.aclweb.org/anthology/2020.sustainlp-1.21 +WS-15,WS-15.46,Counterfactual Augmentation for Training Next Response Selection,Seungtaek Choi|Myeongho Jeong|Jinyoung Yeo|Seung-won Hwang,,38939444,https://www.aclweb.org/anthology/2020.sustainlp-1.22 +WS-15,WS-15.47,Do We Need to Create Big Datasets to Learn a Task?,Swaroop Mishra|Bhavdeep Singh Sachdeva,,38939445,https://www.aclweb.org/anthology/2020.sustainlp-1.23 +WS-15,WS-15.49,Guiding Attention for Self-Supervised Learning with Transformers,Ameet Deshpande|Karthik Narasimhan,,38939446,https://www.aclweb.org/anthology/2020.findings-emnlp.419 +WS-15,WS-15.5,Learning Informative Representations of Biomedical Relations with Latent Variable Models,Harshil Shah|Julien Fauqueur,,38939422,https://www.aclweb.org/anthology/2020.sustainlp-1.3 +WS-15,WS-15.512,Fixed Encoder Self-Attention Patterns in Transformer-Based Machine Translation,Alessandro Raganato,,38940110,https://www.aclweb.org/anthology/2020.findings-emnlp.49 +WS-15,WS-15.547,PBoS: Probabilistic Bag-of-Subwords for Generalizing Word Embedding,Zhao Jinman,,38940115,https://www.aclweb.org/anthology/2020.findings-emnlp.53 +WS-15,WS-15.6,End to End Binarized Neural Networks for Text Classification,Kumar Shridhar|Harshil Jain|Akshat Agarwal|Denis Kleyko,,38939423,https://www.aclweb.org/anthology/2020.sustainlp-1.4 +WS-15,WS-15.651,Pruning Redundant Mappings in Transformer Models via Spectral-Normalized Identity Prior,Zi Lin,,38940112,https://www.aclweb.org/anthology/2020.findings-emnlp.64 +WS-15,WS-15.724,Improving QA Generalization by Concurrent Modeling of Multiple Biases,Mingzhu Wu,,38940113,https://www.aclweb.org/anthology/2020.findings-emnlp.74 +WS-15,WS-15.8,Large Product Key Memory for Pre-trained Language Models,Gyuwan Kim|Tae Hwan Jung,,38939424,https://www.aclweb.org/anthology/2020.findings-emnlp.362 +WS-15,WS-15.9,P-SIF: Document Embeddings using Partition Averaging,Vivek Gupta|Ankit Saw|Pegah Nokhiz|Praneeth Netrapalli|Piyush Rai|Partha Talukdar,,38939425, +WS-15,WS-15.929,ESTeR: Combining Word Co-occurrences and Word Associations for Unsupervised Emotion Detection,Polina Rozenshtein,,38940135,https://www.aclweb.org/anthology/2020.findings-emnlp.93 +WS-16,WS-16.10,Exploring Span Representations in Neural Coreference Resolution,Patrick Kahardipraja|Olena Vyshnevska|Sharid Loáiciga,,38939689,https://www.aclweb.org/anthology/2020.codi-1.4 +WS-16,WS-16.11,Supporting Comedy Writers: Predicting Audience's Response from Sketch Comedy and Crosstalk Scripts,Maolin Li,,38939690,https://www.aclweb.org/anthology/2020.codi-1.5 +WS-16,WS-16.13,Exploring Coreference Features in Heterogeneous Data with Text Classification,Ekaterina Lapshinova-Koltunski|Kerstin Kunz,,38939691,https://www.aclweb.org/anthology/2020.codi-1.6 +WS-16,WS-16.14,Contextualized Embeddings for Connective Disambiguation in Shallow Discourse Parsing,René Knaebel|Manfred Stede,,38939692,https://www.aclweb.org/anthology/2020.codi-1.7 +WS-16,WS-16.15,DSNDM: Deep Siamese Neural Discourse Model with Attention for Text Pairs Categorization and Ranking,Alexander Chernyavskiy|Dmitry Ilvovsky,,38939693,https://www.aclweb.org/anthology/2020.codi-1.8 +WS-16,WS-16.17,Do sentence embeddings capture discourse properties of sentences from Scientific Abstracts ?,Laurine Huber|Chaker Memmadi|Mathilde Dargnat|Yannick Toussaint,,38939694,https://www.aclweb.org/anthology/2020.codi-1.9 +WS-16,WS-16.18,Unsupervised Inference of Data-Driven Discourse Structures using a Tree Auto-Encoder,Patrick Huber|Giuseppe Carenini,,38939695, +WS-16,WS-16.19,Large Discourse Treebanks from Scalable Distant Supervision,Patrick Huber|Giuseppe Carenini,,38939696, +WS-16,WS-16.1951,Adapting Coreference Resolution to Twitter Conversations,Berfin Aktaş,,38940697,https://www.aclweb.org/anthology/2020.findings-emnlp.222 +WS-16,WS-16.20,"Discourse for Argument Mining, and Argument Mining as Discourse",Diane Litman,,38939697, +WS-16,WS-16.208,Converting the Point of View of Messages Spoken to Virtual Assistants,Isabelle G. Lee,,38940694,https://www.aclweb.org/anthology/2020.findings-emnlp.15 +WS-16,WS-16.21,Joint Modeling of Arguments for Event Understanding,Yunmo Chen|Tongfei Chen|Benjamin Van Durme,,38939698,https://www.aclweb.org/anthology/2020.codi-1.10 +WS-16,WS-16.2195,Social Commonsense Reasoning with Multi-Head Knowledge Attention,Debjit Paul,,38940698,https://www.aclweb.org/anthology/2020.findings-emnlp.267 +WS-16,WS-16.22,Analyzing Neural Discourse Coherence Models,Youmna Farag|Josef Valvoda|Helen Yannakoudakis|Ted Briscoe,,38939699,https://www.aclweb.org/anthology/2020.codi-1.11 +WS-16,WS-16.23,Computational Interpretation of Recency for the Choice of Referring Expressions in Discourse,Fahime Same|Kees Van Deemter,,38939700,https://www.aclweb.org/anthology/2020.codi-1.12 +WS-16,WS-16.24,Do We Really Need That Many Parameters In Transformer For Extractive Summarization? Discourse Can Help !,Wen Xiao|Patrick Huber|Giuseppe Carenini,,38939701,https://www.aclweb.org/anthology/2020.codi-1.13 +WS-16,WS-16.2487-WS16,Composed Variational Natural Language Generation for Few-shot Intents,Congying Xia,,38940699,https://www.aclweb.org/anthology/2020.findings-emnlp.303 +WS-16,WS-16.26,Extending Implicit Discourse Relation Recognition to the PDTB-3,Li Liang|Zheng Zhao|Bonnie Webber,,38939702,https://www.aclweb.org/anthology/2020.codi-1.14 +WS-16,WS-16.263,A Hierarchical Network for Abstractive Meeting Summarization with Cross-Domain Pretraining,Chenguang Zhu,,38940695,https://www.aclweb.org/anthology/2020.findings-emnlp.19 +WS-16,WS-16.27,"TED-MDB Lexicons: TrEnConnLex, PtEnConnLex",Murathan Kurfalı|Sibel Ozer|Deniz Zeyrek|Amália Mendes,,38939703,https://www.aclweb.org/anthology/2020.codi-1.15 +WS-16,WS-16.28,Eliminating Mention Overlaps: Evaluation of Coreference Resolution Systems Under Adversarial Attacks,Haixia Chai|Wei Zhao|Steffen Eger|Michael Strube,,38939704,https://www.aclweb.org/anthology/2020.codi-1.16 +WS-16,WS-16.29,Exploring aspects of similarity between spoken personal narratives by disentangling them into narrative clause types,Belen Saldias|Deb Roy,,38939705, +WS-16,WS-16.31,Coreference for Discourse Parsing: A Neural Approach,Grigorii Guz|Giuseppe Carenini,,38939706,https://www.aclweb.org/anthology/2020.codi-1.17 +WS-16,WS-16.3452,Thinking Like a Skeptic: Defeasible Inference in Natural Language,Rachel Rudinger,"Defeasible inference is a mode of reasoning in which an inference (X is a bird, therefore X flies) may be weakened or overturned in light of new evidence (X is a penguin). Though long recognized in classical AI and philosophy, defeasible inference has not been extensively studied in the context of contemporary data-driven research on natural language inference and commonsense reasoning. We introduce Defeasible NLI (abbreviated \delta-NLI), a dataset for defeasible inference in natural language. Defeasible NLI contains extensions to three existing inference datasets covering diverse modes of reasoning: common sense, natural language inference, and social norms. From Defeasible NLI, we develop both a classification and generation task for defeasible inference, and demonstrate that the generation task is much more challenging. Despite lagging human performance, however, generative models trained on this data are capable of writing sentences that weaken or strengthen a specified inference up to 68% of the time.",38940700,https://www.aclweb.org/anthology/2020.findings-emnlp.418 +WS-16,WS-16.3598,Paraphrasing vs Coreferring: Two Sides of the Same Coin,Yehudit Meged,,38940701,https://www.aclweb.org/anthology/2020.findings-emnlp.440 +WS-16,WS-16.4,How does discourse affect Spanish-Chinese Translation? A case study based on a Spanish-Chinese parallel corpus,Shuyuan Cao,,38939686,https://www.aclweb.org/anthology/2020.codi-1.1 +WS-16,WS-16.475,Dialogue Generation on Infrequent Sentence Functions via Structured Meta-Learning,Yifan Gao,,38940696,https://www.aclweb.org/anthology/2020.findings-emnlp.40 +WS-16,WS-16.6,Free the Plural: Unrestricted Split-Antecedent Anaphora Resolution,Juntao Yu|Nafise Sadat Moosavi|Silviu Paun|Massimo Poesio,,38940702, +WS-16,WS-16.7,Multitask Learning-Based Neural Bridging Reference Resolution,Juntao Yu|Massimo Poesio,,38940703, +WS-16,WS-16.8,Beyond Adjacency Pairs: Extracting Longer Regularities in Human-Machine Dialogues,Maitreyee Maitreyee,,38939687,https://www.aclweb.org/anthology/2020.codi-1.2 +WS-16,WS-16.9,Using Type Information to Improve Entity Coreference Resolution,Sopan Khosla|Carolyn Rose,,38939688,https://www.aclweb.org/anthology/2020.codi-1.3 +WS-17,WS-17.10,A Novel Methodology for Developing Automatic Harassment Classifiers for Twitter,Ishaan Arora|Julia Guo|Sarah Ita Levitan|Susan McGregor|Julia Hirschberg,,38939517,https://www.aclweb.org/anthology/2020.alw-1.2 +WS-17,WS-17.13,Countering hate on social media: Large scale classification of hate and counter speech,Joshua Garland|Keyan Ghazi-Zahedi|Jean-Gabriel Young|Laurent Hébert-Dufresne|Mirta Galesic,,38939518,https://www.aclweb.org/anthology/2020.alw-1.13 +WS-17,WS-17.15,Impact of politically biased data on hate speech classification,Maximilian Wich|Jan Bauer|Georg Groh,,38939519,https://www.aclweb.org/anthology/2020.alw-1.7 +WS-17,WS-17.16,A Unified Taxonomy of Harmful Content,Michele Banko|Brendon MacKeen|Laurie Ray,,38939520,https://www.aclweb.org/anthology/2020.alw-1.16 +WS-17,WS-17.19,In Data We Trust: A Critical Analysis of Hate Speech Detection Datasets,Kosisochukwu Madukwe|Xiaoying Gao|Bing Xue,,38939521,https://www.aclweb.org/anthology/2020.alw-1.18 +WS-17,WS-17.2,Moderating Our (Dis)Content: Renewing the Regulatory Approach,Claire Pershan,,38939516,https://www.aclweb.org/anthology/2020.alw-1.14 +WS-17,WS-17.2217,Findings: A little goes a long way: Improving toxic language classification despite data scarcity,Mika Juuti|Tommi Gröndahl|Adrian Flanagan|N. Asokan,,38941227,https://www.aclweb.org/anthology/2020.findings-emnlp.269 +WS-17,WS-17.24,Fine-tuning BERT for multi-domain and multi-label incivil language detection,Kadir Bulut Ozler|Kate Kenski|Steve Rains|Yotam Shmargad|Kevin Coe|Steven Bethard,,38939522,https://www.aclweb.org/anthology/2020.alw-1.4 +WS-17,WS-17.25,Six Attributes of Unhealthy Conversations,Ilan Price|Jordan Gifford-Moore|Jory Flemming|Saul Musker|Maayan Roichman|Guillaume Sylvain|Nithum Thain|Lucas Dixon|Jeffrey Sorensen,,38939523,https://www.aclweb.org/anthology/2020.alw-1.15 +WS-17,WS-17.31,Reducing Unintended Identity Bias in Russian Hate Speech Detection,Nadezhda Zueva|Madina Kabirova|Pavel Kalaidin,,38939524,https://www.aclweb.org/anthology/2020.alw-1.8 +WS-17,WS-17.32,Investigating takedowns of abuse on Twitter,Rosalie Gillett|Nicolas Suzor|Jean Burgess|Bridget Harris|Molly Dragiewicz,,38939525, +WS-17,WS-17.37,Detecting East Asian Prejudice on Social Media,Bertie Vidgen|Scott Hale|Ella Guest|Helen Margetts|David Broniatowski|Zeerak Waseem|Austin Botelho|Matthew Hall|Rebekah Tromble,,38939526,https://www.aclweb.org/anthology/2020.alw-1.19 +WS-17,WS-17.39,Investigating Sampling Bias in Abusive Language Detection,Dante Razo|Sandra Kübler,,38939527,https://www.aclweb.org/anthology/2020.alw-1.9 +WS-17,WS-17.42,Is your toxicity my toxicity? Understanding the influence of rater identity on perceptions of toxicity,Ian Kivlichan|Olivia Redfield|Rachel Rosen|Raquel Saxe|Nitesh Goyal|Lucy Vasserman,,38939528, +WS-17,WS-17.43,Free Expression by Design: Improving in-platform mechanisms and third-party tools to tackle online abuse,Viktorya Vilk|Elodie Vialle|Matt Bailey,,38939529, +WS-17,WS-17.44,HurtBERT: Incorporating Lexical Features with BERT for the Detection of Abusive Language,Anna Koufakou|Endang Wahyu Pamungkas|Valerio Basile|Viviana Patti,,38939530,https://www.aclweb.org/anthology/2020.alw-1.5 +WS-17,WS-17.47,Developing a New Classifier for Automated Identification of Incivility in Social Media,Sam Davidson|Qiusi Sun|Magdalena Wojcieszak,,38939531,https://www.aclweb.org/anthology/2020.alw-1.12 +WS-17,WS-17.48,Abusive Language Detection using Syntactic Dependency Graphs,Kanika Narang|Chris Brew,,38939532,https://www.aclweb.org/anthology/2020.alw-1.6 +WS-17,WS-17.49,Towards a Comprehensive Taxonomy and Large-Scale Annotated Corpus for Online Slur Usage,Jana Kurrek|Haji Mohammad Saleem|Derek Ruths,,38939533,https://www.aclweb.org/anthology/2020.alw-1.17 +WS-17,WS-17.50,Attending the Emotions to Detect Online Abusive Language,Niloofar Safi Samghabadi|Afsheen Hatami|Mahsa Shafaei|Sudipta Kar|Thamar Solorio,,38939534,https://www.aclweb.org/anthology/2020.alw-1.10 +WS-17,WS-17.51,Enhancing the Identification of Cyberbullying through Participant Roles,Gathika Rathnayake|Thushari Atapattu|Mahen Herath|Georgia Zhang|Katrina Falkner,,38939535,https://www.aclweb.org/anthology/2020.alw-1.11 +WS-17,WS-17.52,Using Transfer-based Language Models to Detect Hateful and Offensive Language Online,Vebjørn Isaksen|Björn Gambäck,,38939536,https://www.aclweb.org/anthology/2020.alw-1.3 +WS-17,WS-17.56,On Cross-Dataset Generalization in Automatic Detection of Online Abuse,Isar Nejadgholi|Svetlana Kiritchenko,,38939537,https://www.aclweb.org/anthology/2020.alw-1.20 +WS-17,WS-17.57,Identifying and Measuring Annotator Bias Based on Annotators' Demographic Characteristics,Hala Al Kuwatly|Maximilian Wich|Georg Groh,,38939538,https://www.aclweb.org/anthology/2020.alw-1.21 +WS-17,WS-17.58,Investigating Annotator Bias with a Graph-Based Approach,Maximilian Wich|Hala Al Kuwatly|Georg Groh,,38939539,https://www.aclweb.org/anthology/2020.alw-1.22 +WS-17,WS-17.818,Findings: Hybrid Emoji-Based Masked Language Models for Zero-Shot Abusive Language Detection,Michele Corazza|Stefano Menini|Elena Cabrio|Sara Tonelli|Serena Villata,,38941226,https://www.aclweb.org/anthology/2020.findings-emnlp.84 +WS-18,WS-18.13,Measuring Linguistic Diversity During COVID-19,Jonathan Dunn|Tom Coupe|Benjamin Adams,,38940618,https://www.aclweb.org/anthology/2020.nlpcss-1.1 +WS-18,WS-18.15,How Language Influences Attitudes Toward Brands,David DeFranza|Arul Mishra|Himanshu Mishra,,38940628, +WS-18,WS-18.17,Using BERT for Qualitative Content Analysis in Psychosocial Online Counseling,Philipp Grandeit|Carolyn Haberkern|Maximiliane Lang|Jens Albrecht|Robert Lehmann,,38940609,https://www.aclweb.org/anthology/2020.nlpcss-1.2 +WS-18,WS-18.19,Swimming with the Tide? Positional Claim Detection across Political Text Types,Nico Blokker|Erenay Dayanik|Gabriella Lapesa|Sebastian Padó,,38940616,https://www.aclweb.org/anthology/2020.nlpcss-1.3 +WS-18,WS-18.20,Does Social Support (Expressed in Post Titles) Elicit Comments in Online Substance Use Recovery Forums?,Anietie|Y|Sharath Chandra Guntuku,,38940623,https://www.aclweb.org/anthology/2020.nlpcss-1.4 +WS-18,WS-18.21,I miss you babe: Analyzing Emotion Dynamics During COVID-19 Pandemic,Hui Xian Lynnette Ng|Roy Ka-Wei Lee|Md Rabiul Awal,,38940603,https://www.aclweb.org/anthology/2020.nlpcss-1.5 +WS-18,WS-18.22,"Assessing population-level symptoms of anxiety, depression, and suicide risk in real time using NLP applied to social media data",Alex Fine|Patrick Crutchley|Jenny Blase|Joshua Carroll|Glen Coppersmith,,38940624,https://www.aclweb.org/anthology/2020.nlpcss-1.6 +WS-18,WS-18.23,Topic preference detection: A novel approach to understand perspective taking in conversation,Michael Yeomans|Alison Wood Brooks,,38940626, +WS-18,WS-18.26,Viable Threat on News Reading: Generating Biased News Using Natural Language Models,Saurabh Gupta|Hong Huy Nguyen|Junichi Yamagishi|Isao Echizen,,38940610,https://www.aclweb.org/anthology/2020.nlpcss-1.7 +WS-18,WS-18.28,A Lexical Semantic Leadership Network of Nineteenth CenturyAbolitionist Newspapers,Sandeep Soni|Lauren Klein|Jacob Eisenstein,,38940625, +WS-18,WS-18.29,Unsupervised Anomaly Detection in Parole Hearings using Language Models,Authors: Graham Todd|Catalin Voss|Jenny Hong,,38940611,https://www.aclweb.org/anthology/2020.nlpcss-1.8 +WS-18,WS-18.32,Identifying Worry in Twitter: Beyond Emotion Analysis,Reyha Verma|Christian Von Der Weth|Jithin Vachery|Mohan Kankanhalli,,38940602,https://www.aclweb.org/anthology/2020.nlpcss-1.9 +WS-18,WS-18.33,"Text Zoning and Classification for Job Advertisements in German, French and English",Ann-Sophie Gnehm|Simon Clematide,,38940604,https://www.aclweb.org/anthology/2020.nlpcss-1.10 +WS-18,WS-18.34,Is Wikipedia succeeding in reducing gender bias? Assessing changes in gender bias in Wikipedia using word embeddings,Katja Geertruida Schmahl|Tom Julian Viering|Stavros Makrodimitris|Arman Naseri Jahfari|David Tax|Marco Loog,,38940605,https://www.aclweb.org/anthology/2020.nlpcss-1.11 +WS-18,WS-18.36,Effects of Anonymity on Comment Persuasiveness in Wikipedia Articles for Deletion Discussions,Yimin Xiao|Lu Xiao,,38940619,https://www.aclweb.org/anthology/2020.nlpcss-1.12 +WS-18,WS-18.37,"Uncertainty over Uncertainty: Investigating the Assumptions, Annotations, and Text Measurements of Economic Policy Uncertainty",Katherine Keith|Christoph Teichmann|Brendan O'Connor|Edgar Meij,,38940620,https://www.aclweb.org/anthology/2020.nlpcss-1.13 +WS-18,WS-18.38,Recalibrating classifiers for interpretable abusive content detection,Bertie Vidgen|Scott Hale|Sam Staton|Tom Melham|Helen Margetts|Ohad Kammar|Marcin Szymczak,,38940621,https://www.aclweb.org/anthology/2020.nlpcss-1.14 +WS-18,WS-18.43,Predicting independent living outcomes from written reports of social workers,Angelika Maier|Philipp Cimiano,,38940617,https://www.aclweb.org/anthology/2020.nlpcss-1.15 +WS-18,WS-18.44,Analyzing Political Bias and Unfairness in News Articles at Different Levels of Granularity,Wei-Fan Chen|Khalid Al Khatib|Henning Wachsmuth|Benno Stein,,38940612,https://www.aclweb.org/anthology/2020.nlpcss-1.16 +WS-18,WS-18.45,Mapping Local News Coverage: Precise location extraction in textual news content using fine-tuned BERT based language model,Sarang Gupta|Kumari Nishu,,38940613,https://www.aclweb.org/anthology/2020.nlpcss-1.17 +WS-18,WS-18.47,Foreigner-directed speech is simpler than native-directed: Evidence from social media,Aleksandrs Berdicevskis,,38940614,https://www.aclweb.org/anthology/2020.nlpcss-1.18 +WS-18,WS-18.49,Diachronic Embeddings for People in the News,Felix Hennig|Steven Wilson,,38940606,https://www.aclweb.org/anthology/2020.nlpcss-1.19 +WS-18,WS-18.50,Social media data as a lens onto care-seeking behavior among women veterans of the US armed forces,Kacie Kelly|Alex Fine|Glen Coppersmith,,38940615,https://www.aclweb.org/anthology/2020.nlpcss-1.20 +WS-18,WS-18.51,Understanding Weekly COVID-19 Concerns through Dynamic Content-Specific LDA Topic Modeling,Mohammadzaman Zamani|H.|Rew Schwartz|Johannes Eichstaedt|Sharath Chandra Guntuku|Adithya Virinchipuram Ganesan|Sean Clouston|Salvatore Giorgi,,38940607,https://www.aclweb.org/anthology/2020.nlpcss-1.21 +WS-18,WS-18.52,Emoji and Self-Identity in Twitter Bios,Jinhang Li|Giorgos Longinos|Steven Wilson|Walid Magdy,,38940622,https://www.aclweb.org/anthology/2020.nlpcss-1.22 +WS-18,WS-18.53,Analyzing Gender Bias within Narrative Tropes,Dhruvil Gala|Mohammad Omar Khursheed|Hannah Lerner|Brendan O'Connor|Mohit Iyyer,,38940608,https://www.aclweb.org/anthology/2020.nlpcss-1.23 +WS-18,WS-18.57,An Unfair Affinity Toward Fairness: Characterizing 70 Years of Social Biases in B^Hollywood,Kunal Khadilkar|Ashiqur KhudaBukhsh,,38940627, +WS-19,WS-19.12,Evaluation of Machine Translation Methods applied to Medical Terminologies,Konstantinos Skianis|Yann Briand|Florent Desgrippes,,38940042,https://www.aclweb.org/anthology/2020.louhi-1.7 +WS-19,WS-19.13,Not a cute stroke: Analysis of Rule- and Neural Network-based Information Extraction Systems for Brain Radiology Reports,Andreas Grivas|Beatrice Alex|Claire Grover|Richard Tobin|William Whiteley,,38940043,https://www.aclweb.org/anthology/2020.louhi-1.4 +WS-19,WS-19.14,Biomedical Event Extraction as Multi-turn Question Answering,Xing David Wang|Leon Weber|Ulf Leser,,38940044,https://www.aclweb.org/anthology/2020.louhi-1.10 +WS-19,WS-19.15,GGPONC: A Corpus of German Medical Text with Rich Metadata Based on Clinical Practice Guidelines,Florian Borchert|Christina Lohr|Luise Modersohn|Thomas Langer|Markus Follmann|Jan Philipp Sachs|Udo Hahn|Matthieu-P. Schapranow,,38940045,https://www.aclweb.org/anthology/2020.louhi-1.5 +WS-19,WS-19.17,Medical Concept Normalization in User-Generated Texts by Learning Target Concept Embeddings,Katikapalli Subramanyam Kalyan|Sivanesan Sangeetha,,38940046,https://www.aclweb.org/anthology/2020.louhi-1.3 +WS-19,WS-19.2,The Impact of De-identification on Downstream Named Entity Recognition in Clinical Text,Hanna Berg|Aron Henriksson|Hercules Dalianis,,38940038,https://www.aclweb.org/anthology/2020.louhi-1.1 +WS-19,WS-19.24,Defining and Learning Refined Temporal Relations in the Clinical Narrative,Chen Lin|Kristin Wright-Bettner|Timothy Miller|Steven Bethard|Dmitriy Dligach|Martha Palmer|James H. Martin|Guergana Savova,,38940047,https://www.aclweb.org/anthology/2020.louhi-1.12 +WS-19,WS-19.26,Simple Hierarchical Multi-Task Neural End-To-End Entity Linking for Biomedical Text,Maciej Wiatrak|Juha Iso-Sipila,,38940048,https://www.aclweb.org/anthology/2020.louhi-1.2 +WS-19,WS-19.27,Detection of Mental Health from Reddit via Deep Contextualized Representations,Zhengping Jiang|Sarah Ita Levitan|Jonathan Zomick|Julia Hirschberg,,38940049,https://www.aclweb.org/anthology/2020.louhi-1.16 +WS-19,WS-19.28,Information retrieval for animal disease surveillance: a pattern-based approach,Sarah Valentin|Renaud Lancelot|Mathieu Roche,,38940050, +WS-19,WS-19.32,Identifying Personal Experience Tweets of Medication Effects Using Pre-trained RoBERTa Language Model and Its Updating,Minghao Zhu|Youzhe Song|Ge Jin|Keyuan Jiang,,38940051,https://www.aclweb.org/anthology/2020.louhi-1.14 +WS-19,WS-19.35,Detecting Foodborne Illness Complaints in Multiple Languages Using English Annotations Only,Ziyi Liu|Giannis Karamanolakis|Daniel Hsu|Luis Gravano,,38940052,https://www.aclweb.org/anthology/2020.louhi-1.15 +WS-19,WS-19.4,Normalization of Long-tail Adverse Drug Reactions in Social Media,Emmanouil Manousogiannis|Sepideh Mesbah|Alessandro Bozzon|Robert-Jan Sips|Zoltan Szlanik|Selene Baez,,38940039,https://www.aclweb.org/anthology/2020.louhi-1.6 +WS-19,WS-19.44,Context-Aware Automatic Text Simplification of Health Materials in Low-Resource Domains,Tarek Sakakini|Jong Yoon Lee|Aditya Duri|Renato F.L. Azevedo|Victor Sadauskas|Kuangxiao Gu|Suma Bhat|Dan Morrow|James Graumlich|Saqib Walayat|Mark Hasegawa-Johnson|Thomas Huang|Ann Willemsen-Dunlap|Donald Halpin,,38940053,https://www.aclweb.org/anthology/2020.louhi-1.13 +WS-19,WS-19.5,Multitask Learning of Negation and Speculation using Transformers,Aditya Khandelwal|Benita Kathleen Britto,,38940040,https://www.aclweb.org/anthology/2020.louhi-1.9 +WS-19,WS-19.8,An efficient representation of chronological events in medical texts,Andrey Kormilitzin|Nemanja Vaci|Qiang Liu|Hao Ni|Goran Nenadic|Alejo Nevado-Holgado,,38940041,https://www.aclweb.org/anthology/2020.louhi-1.11 +WS-2,WS-2.1,The DiDi Machine Translation System for WMT20,Tanfang Chen|Weiwei Wang|Wenyang Wei|Xing Shi|Xiangang Li|Jieping Ye|Kevin Knight,,38939543, +WS-2,WS-2.100,Score Combination for Improved Parallel Corpus Filtering for Low Resource Conditions,Muhammad ElNokrashy|Amr Hendy|Mohamed Abdelghaffar|Mohamed Afify|Ahmed Tawfik|Hany Hassan Awadalla,,38939612, +WS-2,WS-2.101,Data Selection for Unsupervised Translation of German–Upper Sorbian,Lukas Edman|Antonio Toral|Gertjan Van Noord,,38939613, +WS-2,WS-2.102,Mitigating Gender Bias in Machine Translation with Target Gender Annotations,Artūrs Stafanovičs|Mārcis Pinnis|Toms Bergmanis,,38939614, +WS-2,WS-2.103,Infosys Machine Translation System for WMT20 Similar Language Translation Task,Kamalkumar Rathinasamy|Amanpreet Singh|Balaguru Sivasambagupta|Prajna Prasad Neerchal|Vani Sivasankaran,,38939615, +WS-2,WS-2.104,The ADAPT System Description for the WMT20 News Translation Task,Venkatesh Parthasarathy|Akshai Ramesh|Rejwanul Haque|Andy Way,,38939616, +WS-2,WS-2.105,The ADAPT's Submissions to the WMT20 Biomedical Translation Task,Prashant Nayak|Rejwanul Haque|Andy Way,,38939617, +WS-2,WS-2.107,LIMSI @ WMT 2020,Sadaf Abdul Rauf|José Carlos Rosales Núñez|Minh Quang Pham|François Yvon,,38939618, +WS-2,WS-2.109,Russian-English Bidirectional Machine Translation System,Ariel Xv,,38939619, +WS-2,WS-2.113,Transfer Learning for Related Languages: IIT Delhi's Submissions to the WMT20 Similar Language Translation Task,Lovish Madaan|Soumya Sharma|Parag Singla,,38939620, +WS-2,WS-2.114,Adobe AMPS's Submission for Very Low Resource Supervised Translation Task at WMT20,Keshaw Singh,,38939621, +WS-2,WS-2.115,Alibaba's Submission for the WMT 2020 APE Shared Task: Improving Automatic Post-Editing with Pre-trained Conditional Cross-Lingual BERT,Jiayi Wang|Ke Wang|Kai Fan|Yuqi Zhang|Jun Lu|Xin Ge|Yangbin Shi|Yu Zhao,,38939622, +WS-2,WS-2.116,NMT based Similar Language Translation for Hindi - Marathi,Vandan Mujadia|Dipti Sharma,,38939623, +WS-2,WS-2.117,Facebook AI's WMT20 News Translation Task Submission,Peng-Jen Chen|Ann Lee|Changhan Wang|Naman Goyal|Angela Fan|Mary Williamson|Jiatao Gu,,38939624, +WS-2,WS-2.118,NLPRL System for Very Low Resource Supervised Machine Translation,Rupjyoti Baruah|Rajesh Kumar Mundotiya|Amit Kumar|Anil Kumar Singh,,38939625, +WS-2,WS-2.119,How Should Markup Tags Be Translated?,Greg Hanneman|Georgiana Dinu,,38939626, +WS-2,WS-2.12,Complete Multilingual Neural Machine Translation,Markus Freitag|Orhan Firat,,38939550, +WS-2,WS-2.121,The University of Edinburgh-Uppsala University's Submission to the WMT 2020 Chat Translation Task,Nikita Moghe|Christian Hardmeier|Rachel Bawden,,38939627, +WS-2,WS-2.122,RTM Ensemble Learning Results at Quality Estimation Task,Ergun Biçici,,38939628, +WS-2,WS-2.123,JUST System for WMT20 Chat Translation Task,Roweida Mohammed|Mahmoud Al-Ayyoub|Malak Abdullah,,38939629, +WS-2,WS-2.124,BERGAMOT-LATTE Submissions for the WMT20 Quality Estimation Shared Task,Marina Fomicheva|Shuo Sun|Lisa Yankovskaya|Frédéric Blain|Vishrav Chaudhary|Mark Fishel|Francisco Guzmán|Lucia Specia,,38939630, +WS-2,WS-2.125,Dual Conditional Cross Entropy Scores and LASER Similarity Scores for the WMT20 Parallel Corpus Filtering Shared Task,Felicia Koerner|Philipp Koehn,,38939631, +WS-2,WS-2.128,UoS Participation in the WMT20 Translation of Biomedical Abstracts,Felipe Soares|Delton Vaz,,38939632, +WS-2,WS-2.133,Tilde at WMT 2020: News Task Systems,Rihards Krišlauks|Mārcis Pinnis,,38939633, +WS-2,WS-2.135,NRC Systems for Low Resource German-Upper Sorbian Machine Translation 2020: Transfer Learning with Lexical Modifications,Rebecca Knowles|Samuel Larkin|Darlene Stewart|Patrick Littell,,38939634, +WS-2,WS-2.136,Linguistically Motivated Subwords Improve English-Tamil Translation: University of Groningen's Submission to WMT-2020,Prajit Dhar|Arianna Bisazza|Gertjan Van Noord,,38939635, +WS-2,WS-2.137,The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT,Jörg Tiedemann,,38939636, +WS-2,WS-2.138,Priming Neural Machine Translation,Minh Quang Pham|Jitao Xu|Josep Crego|François Yvon|Jean Senellart,,38939637, +WS-2,WS-2.139,NUIG-Panlingua-KMI Hindi↔Marathi MT Systems for Similar Language Translation Task @ WMT 2020,Atul Kr. Ojha|Priya Rani|Akanksha Bansal|Bharathi Raja Chakravarthi|Ritesh Kumar|John P. McCrae,,38939638, +WS-2,WS-2.14,Combining Sequence Distillation and Transfer Learning for Efficient Low-Resource Neural Machine Translation Models,Raj Dabre|Atsushi Fujita,,38939551, +WS-2,WS-2.141,NRC Systems for the 2020 Inuktitut-English News Translation Task,Rebecca Knowles|Darlene Stewart|Samuel Larkin|Patrick Littell,,38939639, +WS-2,WS-2.142,Translating Similar Languages: Role of Mutual Intelligibility in Multilingual Transformers,Ife Adebara|El Moatez Billah Nagoudi|Muhammad Abdul Mageed,,38939640, +WS-2,WS-2.143,CUNI Systems for the Unsupervised and Very Low Resource Translation Task in WMT20,Ivana Kvapilíková|Tom Kocmi|Ondřej Bojar,,38939641, +WS-2,WS-2.144,The University of Edinburgh's English-Tamil and English-Inuktitut Submissions to the WMT20 News Translation Task,Rachel Bawden|Alexandra Birch|Radina Dobreva|Arturo Oncevay|Antonio Valerio Miceli Barone|Philip Williams,,38939642, +WS-2,WS-2.146,IST-Unbabel Participation in the WMT20 Quality Estimation Shared Task,João Moura|Miguel Vera|Daan Van Stigt|Fabio Kepler|André F. T. Martins,,38939643, +WS-2,WS-2.147,YerevaNN's systems for WMT20 Biomedical Translation Task: The effect of fixing misaligned sentence pairs,Karen Hambardzumyan|Hovhannes Tamoyan|Hrant Khachatrian,,38939644, +WS-2,WS-2.149,Lite Training Strategies for Portuguese-English and English-Portuguese Translation,Alexandre Lopes|Rodrigo Nogueira|Roberto Lotufo|Helio Pedrini,,38939645, +WS-2,WS-2.150,Noising Scheme for Data Augmentation in Automatic Post-Editing,WonKee Lee|Jaehun Shin|Baikjin Jung|Jihyung Lee|Jong-Hyeok Lee,,38939646, +WS-2,WS-2.153,The University of Maryland's Submissions to the WMT20 Chat Translation Task: Searching for More Data to Adapt Discourse-Aware Neural Machine Translation,Calvin Bao|Yow-Ting Shiue|Chujun Song|Jie Li|Marine Carpuat,,38939647, +WS-2,WS-2.155,The AFRL WMT20 News-Translation Systems,Jeremy Gwinnup|Tim Anderson,,38939648, +WS-2,WS-2.156,An exploratory approach to the Parallel Corpus Filtering shared task WMT20,Ankur Kejriwal|Philipp Koehn,,38939649, +WS-2,WS-2.157,Incorporating Terminology Constraints in Automatic Post-Editing,David Wan|Chris Kedzie|Faisal Ladhak|Marine Carpuat|Kathleen McKeown,,38939650, +WS-2,WS-2.158,NJU's submission for WMT2020 QE Shared Task,Qu Cui|Xiang Geng|Shujian Huang|Jiajun Chen,,38939651, +WS-2,WS-2.16,Paraphrase Generation as Zero-Shot Multilingual Translation: Disentangling Semantic Similarity from Lexical and Syntactic Diversity,Brian Thompson|Matt Post,,38939552, +WS-2,WS-2.160,"Evaluating Low Resource Machine Translation: BLEU, character-based or semantic metric?",Chi-kiu Lo,,38939652, +WS-2,WS-2.161,MT reference-less evaluation using YiSi-2 with bilingual mappings of massive multilingual language model learnt from minimal amount of parallel data,Chi-kiu Lo|Samuel Larkin,,38939653, +WS-2,WS-2.162,Contact Relatedness can help improve multilingual NMT: Microsoft STCI-MT @ WMT20,Vikrant Goyal|Anoop Kunchukuttan|Rahul Kejriwal|Siddharth Jain|Amit Bhagwat,,38939654, +WS-2,WS-2.163,A Study of Residual Adapters for Multi-Domain Neural Machine Translation,Minh Quang Pham|Josep Maria Crego|François Yvon|Jean Senellart,,38939655, +WS-2,WS-2.167,FJWU participation for the WMT20 Biomedical Translation Task,Sumbal Naz|Sadaf Abdul Rauf|Noor-e- Hira|Sami Ul Haq,,38939656, +WS-2,WS-2.168,SJTU-NICT's Supervised and Unsupervised Neural Machine Translation Systems for the WMT20 News Translation Task,Zuchao Li|Hai Zhao|Rui Wang|Kehai Chen|Masao Utiyama|Eiichiro Sumita,,38939657, +WS-2,WS-2.169,Iteratively refined statistical sentence alignment and improved bilingualmappings of pretrained multilingual language model for identifying betterparallel MT training data,Chi-kiu Lo|Eric Joanis,,38939658, +WS-2,WS-2.170,Gender Coreference and Bias Evaluation at WMT 2020,Tom Kocmi|Tomasz Limisiewicz|Gabriel Stanovsky,,38939659, +WS-2,WS-2.171,Subword Segmentation and a Single Bridge Language Affect Zero-Shot Neural Machine Translation,Annette Rios|Mathias Müller|Rico Sennrich,,38939660, +WS-2,WS-2.172,"Speed-optimized, Compact Student Models that Distill Knowledge from a Larger Teacher Model: the UEDIN-CUNI Submission to the WMT 2020 News Translation Task",Ulrich Germann|Roman Grundkiewicz|Martin Popel|Radina Dobreva|Nikolay Bogoychev|Kenneth Heafield,,38939661, +WS-2,WS-2.175,PROMT Systems for WMT 2020 Shared News Translation Task,Alexander Molchanov,,38939662, +WS-2,WS-2.177,The University of Edinburgh's submission to the German-to-English and English-to-German Tracks in the WMT 2020 News Translation and Zero-shot Translation Robustness Tasks,Ulrich Germann,,38939663, +WS-2,WS-2.178,Tencent AI Lab Machine Translation Systems for the WMT20 Biomedical Translation Task,Xing Wang|Zhaopeng Tu|Longyue Wang|Shuming Shi,,38939664, +WS-2,WS-2.179,"Machine Translation for English–Inuktitut with Segmentation,Data Acquisition and Pre-Training",Christian Roest|Lukas Edman|Gosse Minnema|Kevin Kelly|Jennifer Spenader|Antonio Toral,,38939665, +WS-2,WS-2.18,Fine-grained linguistic evaluation for state-of-the-art Machine Translation,Eleftherios Avramidis|Vivien Macketanz|Ursula Strohriegel|Aljoscha Burchardt|Sebastian Möller,,38939553, +WS-2,WS-2.180,CUNI Submission for Inuktitut Language in WMT News 2020,Tom Kocmi,,38939666, +WS-2,WS-2.181,The WMT 2020 Shared Tasks in Unsupervised MT and Very Low Resource Supervised MT,Alexander Fraser,,38939667, +WS-2,WS-2.183,CUNI English-Czech and English-Polish Systems in WMT20: Robust Document-Level Training,Martin Popel,,38939668, +WS-2,WS-2.185,Zero-Shot Translation Quality Estimation with Explicit Cross-Lingual Patterns,Lei Zhou|Liang Ding|Koichi Takeda,,38939669, +WS-2,WS-2.187,Tencent Neural Machine Translation Systems for the WMT20 News Translation Task,Shuangzhi Wu|Xing Wang|Longyue Wang|Fangxu Liu|Jun Xie|Zhaopeng Tu|Shuming Shi|Mu Li,,38939670, +WS-2,WS-2.188,Tencent AI Lab Machine Translation Systems for WMT20 Chat Translation Task,Longyue Wang|Zhaopeng Tu|Xing Wang|Li Ding|Liang Ding|Shuming Shi,,38939671, +WS-2,WS-2.189,Findings of the WMT 2020 Shared Task on Automatic Post-Editing,Rajen Chatterjee|Markus Freitag|Matteo Negri|Marco Turchi,,38939672, +WS-2,WS-2.19,When Does Unsupervised Machine Translation Work?,Kelly Marchisio|Kevin Duh|Philipp Koehn,,38939554, +WS-2,WS-2.190,ParBLEU: Augmenting Metrics with Automatic Paraphrases for the WMT'20 Metrics Shared Task,Rachel Bawden|Biao Zhang|Andre Tättar|Matt Post,,38939673, +WS-2,WS-2.191,Findings of the WMT 2020 Shared Task on Chat Translation,M. Amin Farajian|António V. Lopes|André F. T. Martins|Sameen Maruf|Gholamreza Haffari,,38939674, +WS-2,WS-2.192,"Findings of the WMT 2020 Biomedical Translation Shared Task: Basque, Italian and Russian as New Additional Languages",Rachel Bawden|Giorgio Maria Di Nunzio|Cristian Grozea|Inigo Jauregi Unanue|Antonio Jimeno Yepes|Nancy Mah|David Martinez|Aurélie Névéol|Mariana Neves|Maite Oronoz|Olatz Perez-de-Viñaspre|Massimo Piccardi|Roland Roller|Amy Siu|Philippe Thomas|Federica Vezzani|Maika Vicente Navarro|Dina Wiemann|Lana Yeganova,,38939675, +WS-2,WS-2.193,Findings of the WMT 2020 Shared Task on Machine Translation Robustness,Lucia Specia|Zhenhao Li|Juan Pino|Vishrav Chaudhary|Francisco Guzmán|Graham Neubig|Nadir Durrani|Yonatan Belinkov|Philipp Koehn|Hassan Sajjad|Paul Michel|Xian Li,,38939676, +WS-2,WS-2.194,Findings of the WMT 2020 Shared Task on Quality Estimation,Lucia Specia|Frédéric Blain|Marina Fomicheva|Erick Fonseca|Vishrav Chaudhary|Francisco Guzmán|André F. T. Martins,,38939677, +WS-2,WS-2.195,Findings of the WMT 2020 Shared Task on Parallel Corpus Filtering and Alignment,Philipp Koehn|Vishrav Chaudhary|Ahmed El-Kishky|Naman Goyal|Peng-Jen Chen|Francisco Guzmán,,38939678, +WS-2,WS-2.196,Findings of the First Shared Task on Lifelong Learning Machine Translation,Loïc Barrault|Magdalena Biesialska|Marta R. Costa-jussà|Fethi Bougares|Olivier Galibert,,38939679, +WS-2,WS-2.197,Findings of the 2020 Conference on Machine Translation (WMT20),Loïc Barrault|Magdalena Biesialska|Ondřej Bojar|Marta R. Costa-jussà|Christian Federmann|Yvette Graham|Roman Grundkiewicz|Barry Haddow|Matthias Huck|Eric Joanis|Tom Kocmi|Philipp Koehn|Chi-kiu Lo|Nikola Ljubešić|Christof Monz|Makoto Morishita|Masaaki Nagata|Toshiaki Nakazawa|Santanu Pal|Matt Post|Marcos Zampieri,,38939680, +WS-2,WS-2.1971,Findings of the 2020 Conference on Machine Translation (WMT20): News Translation Task,Christian Federmann|Yvette Graham|Roman Grundkiewicz|Barry Haddow|Tom Kocmi,,38940635, +WS-2,WS-2.1972,Findings of the 2020 Conference on Machine Translation (WMT20): Similar Language Translation Task,Magdalena Biesialska|Marta R. Costa-jussà|Nikola Ljubešić|Santanu Pal|Marcos Zampieri,,38940636, +WS-2,WS-2.1973,Findings of the 2020 Conference on Machine Translation (WMT20): Test Suites,Ondřej Bojar,,38940637, +WS-2,WS-2.198,Learning to Evaluate Translation Beyond English: BLEURT Submissions to the WMT Metrics 2020 Shared Task,Thibault Sellam|Amy Pu|Hyung Won Chung|Sebastian Gehrmann|Qijun Tan|Markus Freitag|Dipanjan Das|Ankur Parikh,,38939681, +WS-2,WS-2.2,Volctrans Parallel Corpus Filtering System for WMT 2020,Runxin Xu|Zhuo Zhi|Jun Cao|Mingxuan Wang|Lei Li,,38939544, +WS-2,WS-2.20,"Multilingual Neural Machine Translation: Case-study for Catalan, Spanish and Portuguese Romance Languages",Pere Vergés Boncompte|Marta R. Costa-jussà,,38939555, +WS-2,WS-2.21,NICT Kyoto Submission for the WMT'20 Quality Estimation Task: Intermediate Training for Domain and Task Adaptation,Raphael Rubino,,38939556, +WS-2,WS-2.22,On the Same Page? Comparing Inter-Annotator Agreement in Sentence and Document Level Human Machine Translation Evaluation,Sheila Castilho,,38939557, +WS-2,WS-2.23,OPPO's Machine Translation Systems for WMT20,Tingxun Shi|Shiyu Zhao|Xiaopu Li|Xiaoxue Wang|Qian Zhang|Di Ai|Dawei Dang|Xue Zhengshan|Jie Hao,,38939558, +WS-2,WS-2.26,Towards Multimodal Simultaneous Neural Machine Translation,Aizhan Imankulova|Masahiro Kaneko|Tosho Hirasawa|Mamoru Komachi,,38939559, +WS-2,WS-2.3,Tohoku-AIP-NTT at WMT 2020 News Translation Task,Shun Kiyono|Takumi Ito|Ryuto Konno|Makoto Morishita|Jun Suzuki,,38939545, +WS-2,WS-2.31,Document-aligned Japanese-English Conversation Parallel Corpus,Matīss Rikters|Ryokan Ri|Tong Li|Toshiaki Nakazawa,,38939560, +WS-2,WS-2.32,POSTECH-ETRI's Submission to the WMT2020 APE Shared Task:Automatic Post-Editing with Cross-lingual Language Model,Jihyung Lee|WonKee Lee|Jaehun Shin|Baikjin Jung|Young-Kil Kim|Jong-Hyeok Lee,,38939561, +WS-2,WS-2.35,Pretrained Language Models and Backtranslation for English-Basque Biomedical Neural Machine Translation,Inigo Jauregi Unanue|Massimo Piccardi,,38939562, +WS-2,WS-2.36,WMT20 Document-Level Markable Error Exploration,Vilém Zouhar|Tereza Vojtěchová|Ondřej Bojar,,38939563, +WS-2,WS-2.37,The LMU Munich System for the WMT20 Very Low Resource Supervised MT Task,Jindřich Libovický|Viktor Hangya|Helmut Schmid|Alexander Fraser,,38939564, +WS-2,WS-2.39,Incorporate Semantic Structures into Machine Translation Evaluation via UCCA,Jin Xu|Yinuo Guo|Junfeng Hu,,38939565, +WS-2,WS-2.40,Samsung R D Institute Poland submission to WMT20 News Translation Task,Mateusz Krubiński|Marcin Chochowski|Bartłomiej Boczek|Mikołaj Koszowski|Adam Dobrowolski|Marcin Szymański|Paweł Przybysz,,38939566, +WS-2,WS-2.42,The MUCOW word sense disambiguation test suite at WMT 2020,Yves Scherrer|Alessandro Raganato|Jörg Tiedemann,,38939567, +WS-2,WS-2.43,Language Models not just for Pre-training: Fast Online Neural Noisy Channel Modeling,Shruti Bhosale|Kyra Yee|Sergey Edunov|Michael Auli,,38939568, +WS-2,WS-2.44,Bicleaner at WMT 2020: Universitat d'Alacant–Prompsit's submission to the parallel corpus filtering shared task,Miquel Esplà-Gomis|Víctor M. Sánchez-Cartagena|Jaume Zaragoza-Bernabeu|Felipe Sánchez-Martínez,,38939569, +WS-2,WS-2.45,HW-TSC's Participation at WMT 2020 Automatic Post Editing Shared Task,Hao Yang|Minghan Wang|Daimeng Wei|Hengchao Shang|Jiaxin Guo|Zongyao Li|Lizhi Lei|Ying Qin|Shimin Tao|Shiliang Sun|Yimeng Chen,,38939570, +WS-2,WS-2.46,HW-TSC's Participation at WMT 2020 Quality Estimation Shared Task,Minghan Wang|Hao Yang|Hengchao Shang|Daimeng Wei|Jiaxin Guo|Lizhi Lei|Ying Qin|Shimin Tao|Shiliang Sun|Yimeng Chen|Liangyou Li,,38939571, +WS-2,WS-2.47,The NiuTrans Machine Translation Systems for WMT20,Yuhao Zhang|Ziyang Wang|Runzhe Cao|Binghao Wei|Weiqiao Shan|Shuhan Zhou|Abudurexiti Reheman|Tao Zhou|Xin Zeng|Laohu Wang|Yongyu Mu|Jingnan Zhang|Xiaoqian Liu|Xuanjun Zhou|Yinqiao Li|Bei Li|Tong Xiao|Jingbo Zhu,,38939572, +WS-2,WS-2.48,HW-TSC's Participation in the WMT 2020 News Translation Shared Task,Daimeng Wei|Hengchao Shang|Zhanglin Wu|Zhengzhe Yu|Liangyou Li|Jiaxin Guo|Minghan Wang|Hao Yang|Lizhi Lei|Ying Qin|Shiliang Sun,,38939573, +WS-2,WS-2.49,eTranslation's Submissions to the WMT 2020 News Translation Task,Csaba Oravecz|Katina Bontcheva|László Tihanyi|David Kolovratnik|Bhavani Bhaskar|Adrien Lardilleux|Szymon Klocek|Andreas Eisele,,38939574, +WS-2,WS-2.5,Two-Phase Cross-Lingual Language Model Fine-Tuning for Machine Translation Quality Estimation,Dongjun Lee,,38939546, +WS-2,WS-2.51,The NITS-CNLP System for the Unsupervised MT Task at WMT 2020,Salam Michael Singh|Thoudam Doren Singh|Sivaji Bandyopadhyay,,38939575, +WS-2,WS-2.52,Huawei's Submissions to the WMT20 Biomedical Translation Task,Wei Peng|Jianfeng Liu|Minghan Wang|Liangyou Li|Xupeng Meng|Hao Yang|Qun Liu,,38939576, +WS-2,WS-2.53,"Naver Labs Europe's Participation to the Robustness, Chat and Biomedical Tasks at WMT 2020",Alexandre Berard|Ioan Calapodescu|Vassilina Nikoulina|Jerin Philip,,38939577, +WS-2,WS-2.54,Neural Machine Translation between similar South-Slavic languages,Maja Popović|Alberto Poncelas,,38939578, +WS-2,WS-2.57,IIE's Neural Machine Translation Systems for WMT20,Xiangpeng Wei|Ping Guo|Yunpeng Li|Xingsheng Zhang|Luxi Xing|Yue Hu,,38939579, +WS-2,WS-2.58,Alibaba Submission to the WMT20 Parallel Corpus Filtering Task,Jun Lu|Xin Ge|Yangbin Shi|Yuqi Zhang,,38939580, +WS-2,WS-2.59,The Volctrans Machine Translation System for WMT20,Liwei Wu|Xiao Pan|Zehui Lin|Yaoming Zhu|Mingxuan Wang|Lei Li,,38939581, +WS-2,WS-2.6,Cross-Lingual Transformers for Neural Automatic Post-Editing,Dongjun Lee,,38939547, +WS-2,WS-2.60,The LMU Munich System for the WMT 2020 Unsupervised Machine Translation Shared Task,Alexandra Chronopoulou|Dario Stojanovski|Viktor Hangya|Alexander Fraser,,38939582, +WS-2,WS-2.62,Addressing Exposure Bias With Document Minimum Risk Training: Cambridge at the WMT20 Biomedical Translation Task,Danielle Saunders|Bill Byrne,,38939583, +WS-2,WS-2.65,UdS-DFKI@WMT20: Unsupervised MT and Very Low Resource Supervised MT for German↔Upper Sorbian,Sourav Dutta|Jesujoba Alabi|Saptarashmi Bandyopadhyay|Dana Ruiter|Josef Van Genabith,,38939584, +WS-2,WS-2.68,Diving Deep into Context-Aware Neural Machine Translation,Jingjing Huo|Christian Herold|Yingbo Gao|Leonard Dahlmann|Shahram Khadivi|Hermann Ney,,38939585, +WS-2,WS-2.69,The DeepMind Chinese–English Document Translation System at WMT2020,Lei Yu|Laurent Sartran|Po-Sen Huang|Wojciech Stokowiec|Domenic Donato|Srivatsan Srinivasan|Alek Andreev|Wang Ling|Sona Mokra|Agustin Dal Lago|Yotam Doron|Susannah Young|Phil Blunsom|Chris Dyer,,38939586, +WS-2,WS-2.70,Neural Machine Translation for Similar Language Translation: The Case of Indo-Aryan Languages,Santanu Pal|Marcos Zampieri,,38939587, +WS-2,WS-2.71,Fast Interleaved Bidirectional Sequence Generation,Biao Zhang|Ivan Titov|Rico Sennrich,,38939588, +WS-2,WS-2.72,The University of Helsinki and Aalto University submissions to the WMT 2020 news and low-resource translation tasks,Yves Scherrer|Stig-Arne Grönroos|Sami Virpioja,,38939589, +WS-2,WS-2.73,A3-108 Machine Translation System for Similar Language Translation Shared Task 2020,Saumitra Yadav|Manish Shrivastava,,38939590, +WS-2,WS-2.74,Elhuyar submission to the Biomedical Translation Task 2020 onterminology and abstracts translation,Ander Corral|Xabier Saralegi,,38939591, +WS-2,WS-2.75,Attention Transformer Model for Translation of Similar Languages,Farhan Dhanani|Muhammad Rafi,,38939592, +WS-2,WS-2.76,Human-Paraphrased References Improve Neural Machine Translation,Markus Freitag|George Foster|David Grangier|Colin Cherry,,38939593, +WS-2,WS-2.77,The TALP-UPC Machine Translation Systems for WMT20 News Translation Task: Multilingual Adaptation for Low Resource MT,Carlos Escolano|Marta R. Costa-jussà|José A. R. Fonollosa,,38939594, +WS-2,WS-2.8,Towards a Better Evaluation of Metrics for Machine Translation,Peter Stanchev|Weiyue Wang|Hermann Ney,,38939548, +WS-2,WS-2.80,The IPN-CIC team system submission for the WMT 2020 simmilar language task,Luis A. Menéndez-Salazar|Grigori Sidorov|Marta R. Costa-Jussà,,38939595, +WS-2,WS-2.81,WeChat Neural Machine Translation Systems for WMT20,Fandong Meng|Jianhao Yan|Yijin Liu|Yuan Gao|Xianfeng Zeng|Qinsong Zeng|Peng Li|Ming Chen|Jie Zhou|Sifan Liu|Hao Zhou,,38939596, +WS-2,WS-2.82,Look It Up: Bilingual and Monolingual Dictionaries Improve Neural Machine Translation,Xing Jie Zhong|David Chiang,,38939597, +WS-2,WS-2.83,Low-Resource Translation as Language Modeling,Tucker Berckmann|Berkan Hiziroglu,,38939598, +WS-2,WS-2.84,An Iterative Knowledge Transfer NMT System for WMT20 News Translation Task,Jiwan Kim|Soyoon Park|Sangha Kim|Yoonjung Choi,,38939599, +WS-2,WS-2.86,Combination of Neural Machine Translation Systems at WMT20,Benjamin Marie|Raphael Rubino|Atsushi Fujita,,38939600, +WS-2,WS-2.87,The NiuTrans System for the WMT20 Quality Estimation Shared Task,Chi Hu|Hui Liu|Kai Feng|Chen Xu|Nuo Xu|Zefan Zhou|Shiqin Yan|Yingfeng Luo|Chenglong Wang|Xia Meng|Tong Xiao|Jingbo Zhu,,38939601, +WS-2,WS-2.88,TMUOU submission for WMT20 Quality Estimation Shared Task,Akifumi Nakamachi|Hiroki Shimanaka|Tomoyuki Kajiwara|Mamoru Komachi,,38939602, +WS-2,WS-2.89,GTCOM Neural Machine Translation Systems for WMT20,Chao Bei|Hao Zong|Qingmin Liu|Conghu Yuan,,38939603, +WS-2,WS-2.9,Ixamed's submission description for WMT20 Biomedical shared task: benefits and limitations of using terminologies for domain adaptation,Xabier Soto|Olatz Perez-de-Viñaspre|Gorka Labaka|Maite Oronoz,,38939549, +WS-2,WS-2.90,Unbabel's Participation in the WMT20 Metrics Shared Task,Ricardo Rei|Craig Stewart|Ana C Farinha|Alon Lavie,,38939604, +WS-2,WS-2.91,Transformer-based Neural Machine Translation System for Hindi - Marathi,Amit Kumar|Rupjyoti Baruah|Rajesh Kumar Mundotiya|Anil Kumar Singh,,38939605, +WS-2,WS-2.92,Filtering Noisy Parallel Corpus using Transformers with Proxy Task Learning,Haluk Açarçiçek|Talha Çolakoğlu|pınar Ece Aktan hatipoğlu|Chong Hsuan Huang|Wei Peng,,38939606, +WS-2,WS-2.93,TransQuest at WMT2020: Sentence-Level Direct Assessment,Tharindu Ranasinghe|Constantin Orasan|Ruslan Mitkov,,38939607, +WS-2,WS-2.94,Document Level NMT of Low-Resource Languages with Backtranslation,Sami Ul Haq|Sadaf Abdul Rauf|Arsalan Shaukat|Abdullah Saeed,,38939608, +WS-2,WS-2.96,Tencent submission for WMT20 Quality Estimation Shared Task,Haijiang Wu|Zixuan Wang|Qingsong Ma|Xinjie Wen|Ruichen Wang|Xiaoli Wang|Yulin Zhang|Zhipeng Yao|Siyao Peng,,38939609, +WS-2,WS-2.97,PATQUEST: Papago Translation Quality Estimation,Yujin Baek|Zae Myung Kim|Jihyung Moon|Hyunjoong Kim|Eunjeong Park,,38939610, +WS-2,WS-2.99,Hindi-Marathi Cross Lingual Model,Sahinur Rahman Laskar|Abdullah Faiz Ur Rahman Khilji|Partha Pakray|Sivaji Bandyopadhyay,,38939611, +WS-20,WS-20.13,"Probabilistic Extension of Precision, Recall, and F1 Score for More Thorough Evaluation of Classification Models",Reda Yacouby|Dustin Axman,,38939710, +WS-20,WS-20.15,Truth or Error? Towards systematic analysis of factual errors in abstractive summaries,Klaus-Michael Lux|Maya Sappelli|Martha Larson,,38939711, +WS-20,WS-20.16,Evaluating Word Embeddings on Low-Resource Languages,Nathan Stringham|Mike Izbicki,,38939712, +WS-20,WS-20.1663,Automatically Identifying Gender Issues in Machine Translation using Perturbations,Hila Gonen|Kellie Webster,,38940033, +WS-20,WS-20.18,"Best Practices for Crowd-based Evaluation of German Summarization: Comparing Crowd, Expert and Automatic Evaluation",Neslihan Iskender|Tim Polzehl|Sebastian Möller,,38939713, +WS-20,WS-20.183,GRUEN for Evaluating Linguistic Quality of Generated Text,Wanzheng Zhu|Suma Bhat,,38940645, +WS-20,WS-20.21,Fill in the BLANC: Human-free quality estimation of document summaries,Oleg Vasilyev|Vedant Dharnidharka|John Bohannon,,38939714, +WS-20,WS-20.22,Are Some Words Worth More than Others?,Shiran Dudy|Steven Bedrick,,38939715, +WS-20,WS-20.23,A survey on Recognizing Textual Entailment as an NLP Evaluation,Adam Poliak,,38939716, +WS-20,WS-20.2311,An Evaluation Method for DiachronicWord Sense Induction,Ashjan Alsulaimani|Erwan Moreau|Carl Vogel,,38940034, +WS-20,WS-20.2378,KoBE: Knowledge-Based Machine Translation Evaluation,Zorik Gekhman|Roee Aharoni|Genady Beryozkin|Markus Freitag|Wolfgang Macherey,,38940035, +WS-20,WS-20.25,One of these words is not like the other: a reproduction of outlier identification using non-contextual word representations,Jesper Brink Andersen|Mikkel Bak Bertelsen|Mikkel Hørby Schou|Manuel R. Ciosici|Ira Assent,,38939717, +WS-20,WS-20.2740,CDEvalSumm: An Empirical Study of Cross-Dataset Evaluation for Neural Summarization Systems,Yiran Chen|Pengfei Liu|Ming Zhong|Zi-Yi Dou|DanqingWang|Xipeng Qiu|Xuanjing Huang,,38940036, +WS-20,WS-20.28,Evaluating the Evaluation of Diversity in Natural Language Generation,Guy Tevet|Jonathan Berant,,38940785, +WS-20,WS-20.29,Item Response Theory for Efficient Human Evaluation of Chatbots,João Sedoc|Lyle Ungar,,38939718, +WS-20,WS-20.3,Artemis: A Novel Annotation Methodology for Indicative Single Document Summarization,Rahul Jha|Keping Bi|Yang Li|Mahdi Pakdaman|Asli Celikyilmaz|Ivan Zhiboedov|Kieran McDonald,,38939707, +WS-20,WS-20.30,ViLBERTScore: Evaluating Image Caption Using Vision-and-Language BERT,Hwanhee Lee|Seunghyun Yoon|Franck Dernoncourt|Doo Soon Kim|Trung Bui|Kyomin Jung,,38939719, +WS-20,WS-20.3117,#TurkihTweets: A Benchmark Dataset for Turkish Text Correction,Asiye Tuba Köksal|Özge Bozal|Emre Yürekli|Gizem Gezici,,38940037, +WS-20,WS-20.3148,Multichannel Generative Language Model: Learning All Possible Factorizations Within and Across Channels,Harris Chan,,38940114, +WS-20,WS-20.34,On Aligning OpenIE Extractions with Knowledge Bases: A Case Study,Kiril Gashteovski|Rainer Gemulla|Bhushan Kotnis|Sven Hertling|Christian Meilicke,,38939720, +WS-20,WS-20.35,Improving Text Generation Evaluation with Batch Centering and Tempered Word Mover Distance,Xi Chen|Nan Ding|Tomer Levinboim|Radu Soricut,,38939721, +WS-20,WS-20.36,On the Evaluation of Machine Translation n-best Lists,Jacob Bremerman|Huda Khayrallah|Douglas Oard|Matt Post,,38939722, +WS-20,WS-20.37,Grammaticality and Language Modelling,Jingcheng Niu|Gerald Penn,,38939723, +WS-20,WS-20.5,ClusterDataSplit: Exploring Challenging Clustering-Based Data Splits for Model Performance Evaluation,Hanna Wecker|Annemarie Friedrich|Heike Adel,,38939708, +WS-20,WS-20.7,BLEU Neighbors: A Reference-less Approach to Automatic Evaluation,Kawin Ethayarajh|Dorsa Sadigh,,38939709, +WS-20,WS-20.8,An Open-Source Library for Using and Developing Summarization Evaluation Metrics,Daniel Deutsch|Dan Roth,,38940784, +WS-20,WS-20.815,A Study in Improving BLEU Reference Coverage with Diverse Automatic Paraphrasing,Rachel Bawden|Biao Zhang|Lisa Yankovskaya|Andre Tättar|Matt Post,,38940032, +WS-21,WS-21.10,Structured Prediction for Joint Class Cardinality and Entity Property Inference in Model-Complete Text Comprehension,Hendrik Ter Horst|Philipp Cimiano,,38940161, +WS-21,WS-21.11,Cascaded Text Generation with Markov Transformers,Yuntian Deng|Alexander Rush,,38940151, +WS-21,WS-21.12,Scaling Hidden Markov Language Models,Justin Chiu|Alexander Rush,,38940160, +WS-21,WS-21.14,Energy-based Neural Modelling for Large-Scale Multiple Domain Dialogue State Tracking,Anh Duong Trinh|Robert J. Ross|John D. Kelleher,,38940154, +WS-21,WS-21.1463,Recurrent Inference in Text Editing,Ning Shi|Ziheng Zeng|Haotian Zhang|Yichen Gong,,38940648, +WS-21,WS-21.16,End-to-End Extraction of Structured Information from Business Documents with Pointer-Generator Networks,Clément Sage|Alex Aussem|Véronique Eglin|Haytham Elghazel|Jérémy Espinas,,38940153, +WS-21,WS-21.17,Syntactically restricted self-attention for Semantic Role Labeling,Zihao Deng|Sijia Wang|Brendan Juba,,38940162, +WS-21,WS-21.1774,Computer Assisted Translation with Neural Quality Estimation and Automatic Post-Editing,Ke Wang|Jiayi Wang|Niyu Ge|Yangbin Shi|Zhao Yu|Kai Fan,,38940649, +WS-21,WS-21.19,Randomized Deep Structured Prediction for Argumentation Mining,Manuel Widmoser|Maria Pacheco|Jean Honorio|Dan Goldwasser,,38940158, +WS-21,WS-21.2,Syntax-driven Iterative Expansion Language Models for Controllable Text Generation,Noe Casas|José A. R. Fonollosa|Marta R. Costa-jussà,,38940163, +WS-21,WS-21.20,Layer-wise Guided Training for BERT: Learning Incrementally Refined Document Representations,Nikolaos Manginas|Ilias Chalkidis|Prodromos Malakasiotis,,38940156, +WS-21,WS-21.21,Improving Joint Training of Inference Networks and Structured Prediction Energy Networks,Lifu Tu|Richard Yuanzhe Pang|Kevin Gimpel,,38940143, +WS-21,WS-21.2146,Graph-to-Tree Neural Networks for Learning Structured Input-Output Translation with Applications to Semantic Parsing and Math Word Problem,Shucheng Li|Lingfei Wu|Shiwei Feng|Fangli Xu|Fengyuan Xu|Sheng Zhong,,38940650, +WS-21,WS-21.22,Reading the Manual: Event Extraction as Definition Comprehension,Yunmo Chen|Tongfei Chen|Seth Ebner|Aaron Steven White|Benjamin Van Durme,,38940159, +WS-21,WS-21.2220,An Instance Level Approach for Shallow Semantic Parsing in Scientific Procedural Text,Daivik Swarup|Ahsaas Bajaj|Sheshera Mysore|Tim O'Gorman|Rajarshi Das|Andrew McCallum,,38940651, +WS-21,WS-21.2284,On Long-Tailed Phenomena in Neural Machine Translation,Vikas Raunak|Siddharth Dalmia|Vivek Gupta|Florian Metze,,38940652, +WS-21,WS-21.23,On the Discrepancy between Density Estimation and Sequence Generation,Jason Lee|Dustin Tran|Orhan Firat|Kyunghyun Cho,,38940144, +WS-21,WS-21.24,Log-Linear Reformulation of the Noisy Channel Model for Document-Level Neural Machine Translation,Sébastien Jean|Kyunghyun Cho,,38940157, +WS-21,WS-21.2417,Graph-to-Graph Transformer for Transition-based Dependency Parsing,Alireza Mohammadshahi|James Henderson,,38940653, +WS-21,WS-21.26,Deeply Embedded Knowledge Representation Reasoning For Natural Language Question Answering: A Practitioner's Perspective,Arindam Mitra|Sanjay Narayana|Chitta Baral,,38940152, +WS-21,WS-21.3373,An Empirical Investigation of Beam-Aware Training in Supertagging,Renato Negrinho|Matthew R. Gormley|Geoff Gordon,,38940654, +WS-21,WS-21.4,CopyNext: Explicit Span Copying and Alignment in Sequence to Sequence Models,Abhinav Singh|Patrick Xia|Guanghui Qin|Mahsa Yarmohammadi|Benjamin Van Durme,,38940142, +WS-21,WS-21.7,Generating Synthetic Data for Task-Oriented Semantic Parsing with Hierarchical Representations,Ke Tran|Ming Tan,,38940155, +WS-21,WS-21.957,Multi^2OIE: Multilingual Open Information Extraction based on Multi-Head Attention with BERT,Youngbin Ro|Yukyung Lee|Pilsung Kang,,38940647, +WS-21,WS-21.WS21-490,A Fully Hyperbolic Neural Model for Hierarchical Multi-Class Classification,Federico López|Michael Strube,,38940646, +WS-23,WS-23.106,Towards End-to-End In-Image Neural Machine Translation,Elman Mansimov|Mitchell Stern|Mia Chen|Orhan Firat|Jakob Uszkoreit|Puneet Jain,,38939782, +WS-23,WS-23.110,Modulated Fusion using Transformer for Linguistic-Acoustic Emotion Recognition,Jean-Benoit Delbrouck|Noé Tits|Stéphane Dupont,,38939779, +WS-23,WS-23.114,Multimodal Speech Recognition with Unstructured Audio Masking,Tejas Srinivasan|Ramon Sanabria|Florian Metze|Desmond Elliott,,38939780, +WS-23,WS-23.119,MAST: Multimodal Abstractive Summarization with Trimodal Hierarchical Attention,Aman Khullar|Udit Arora,,38939781, +WS-23,WS-23.122,Reasoning Over History: Context Aware Visual Dialog,Muhammad Shah|Shikib Mehri|Tejas Srinivasan,,38939783, +WS-23,WS-23.1589,No Gestures Left Behind: Learning Relationships between Spoken Language and Freeform Gestures,Chaitanya Ahuja Et Al,,38940175, +WS-23,WS-23.3273,Beyond Language: Learning Commonsense from Images for Reasoning,Yanyan Lan Et Al,,38940176, +WS-23,WS-23.Loic,A Vision on (Simultaneous) Multimodal Machine Translation,Loic Barrault,,38939784, +WS-24,WS-24.1,On Log-Loss Scores and (No) Privacy,Abhinav Aggarwal|Zekun Xu|Oluwaseyi Feyisetan|Nathanael Teissier,,38939769, +WS-24,WS-24.11,Surfacing Privacy Settings Using Semantic Matching,Rishabh Khandelwal|Asmit Nayak|Yao Yao|Kassem Fawaz,,38939773, +WS-24,WS-24.12,Differentially Private Language Models Benefit from Public Pre-training,Gavin Kerrigan|Dylan Slack|Jens Tuyls,,38939774, +WS-24,WS-24.2,A Differentially Private Text Perturbation Method Using Regularized Mahalanobis Metric,Zekun Xu|Abhinav Aggarwal|Oluwaseyi Feyisetan|Nathanael Teissier,,38939770, +WS-24,WS-24.2534,A Semantics-based Approach to Disclosure Classification in User-Generated Online Content,Chandan Akiti,,38940639, +WS-24,WS-24.3,TextHide: Tackling Data Privacy in Language Understanding Tasks,Yangsibo Huang|Zhao Song|Danqi Chen|Kai Li|Sanjeev Arora,,38939771, +WS-24,WS-24.9,Identifying and Classifying Third-party Entities in Natural Language Privacy Policies,Mitra Bokaie Hosseini|Pragyan K C|Irwin Reyes|Serge Egelman,,38939772, +WS-25,WS-25.33,The elephant in the interpretability room: Why use attention as explanation when we have saliency methods?,Jasmijn Bastings|Katja Filippova,,38939764, +WS-25,WS-25.43,Dissecting Lottery Ticket Transformers: Structural and Behavioral Study of Sparse Neural Machine Translation,Rajiv Movva|Jason Zhao,,38939765, +WS-25,WS-25.45,BERTs of a feather do not generalize together: Large variability in generalization across models with similar test set performance,R. Thomas Mccoy|Junghyun Min|Tal Linzen,,38939766, +WS-25,WS-25.54,The EOS Decision and Length Extrapolation,Benjamin Newman|John Hewitt|Percy Liang|Christopher D. Manning,,38939767, +WS-25,WS-25.59,Evaluating Attribution Methods using White-Box LSTMs,Yiding Hao,,38939768, +WS-25,WS-25.70,diagNNose: A Library for Neural Activation Analysis,Jaap Jumelet,,38940638, +WS-25,WS-25.8,What Happens To BERT Embeddings During Fine-tuning?,Amil Merchant|Elahe Rahimtoroghi|Ellie Pavlick|Ian Tenney,,38939763, +WS-26,WS-26.1,COVID-19 Surveillance through Twitter using Self-Supervised Learning and Few Shot Learning,Brandon Lwowski|Peyman Najafirad,,38939841, +WS-26,WS-26.10,Frugal neural reranking: evaluation on the Covid-19 literature,Tiago Almeida|Sérgio Matos,,38939845, +WS-26,WS-26.16,CORA: A Deep Active Learning Covid-19 Relevancy Algorithm to Identify Core Scientific Articles,Zubair Afzal|Vikrant Yadav|Olga Fedorova|Vaishnavi Kandala|Janneke Van De Loo|Saber A Akhondi|Pascal Coupet|George Tsatsaronis,,38939846, +WS-26,WS-26.2,A Multilingual Neural Machine Translation Model for Biomedical Data,Alexandre Berard|Zae Myung Kim|Vassilina Nikoulina|Eunjeong Lucy Park|Matthias Gallé,,38939842, +WS-26,WS-26.20,Quantifying the Effects of COVID-19 on Mental Health Support Forums,Laura Biester|Katie Matton|Janarthanan Rajendran|Emily Mower|Rada Mihalcea,,38939847, +WS-26,WS-26.28,Exploratory Analysis of COVID-19 Related Tweets in North America to Inform Public Health Institutes,Hyeju Jang|Emily Rempel|Giuseppe Carenini|Naveed Janjua,,38939848, +WS-26,WS-26.31,Not Just Semantics: Social Distancing and COVID Discourse on Twitter,Austin Van Loon|Sheridan Stewart|Brandon Waldon|Shrinidhi K Lakshmikanth|Ishan Shah|Sharath Chandra Guntuku|Garrick Sherman|James Zou|Johannes Eichstaedt,,38939849, +WS-26,WS-26.35,Expressive Interviewing: A Conversational System for Coping with COVID-19,Charles Welch|Allison Lahnala|Veronica Perez-Rosas|Siqi Shen|Sarah Seraj|Larry An|Kenneth Resnicow|James Pennebaker|Rada Mihalcea,,38939850, +WS-26,WS-26.37,Detecting COVID-19 Misinformation on Social Media,Tamanna Hossain|Robert L. Logan Iv|Arjuna Ugarte|Yoshitomo Matsubara|Sean Young|Sameer Singh,,38939851, +WS-26,WS-26.45,A System for Worldwide COVID-19 Information Aggregation,Akiko Aizawa|Frederic Bergeron|Junjie Chen|Fei Cheng|Katsuhiko Hayashi|Kentaro Inui|Hiroyoshi Ito|Daisuke Kawahara|Masaru Kitsuregawa|Hirokazu Kiyomaru|Masaki Kobayashi|Takashi Kodama|Sadao Kurohashi|Qianying Liu|Masaki Matsubara|Yusuke Miyao|Atsuyuki Morishima|Yugo Murawaki|Kazumasa Omura|Haiyue Song|Eiichiro Sumita|Shinji Suzuki|Ribeka Tanaka|Yu Tanaka|Masashi Toyoda|Nobuhiro Ueda|Honai Ueoka|Masao Utiyama|Ying Zhong,,38939852, +WS-26,WS-26.47,Temporal Mental Health Dynamics on Social Media,Tom Tabak|Matthew Purver,,38939853, +WS-26,WS-26.50,TICO-19: the Translation Initiative for COvid-19,Antonios Anastasopoulos|Alessandro Cattelan|Zi-Yi Dou|Marcello Federico|Christian Federmann|Dmitriy Genzel|Franscisco Guzmán|Junjie Hu|Macduff Hughes|Philipp Koehn|Rosie Lazar|Will Lewis|Graham Neubig|Mengmeng Niu|Alp Öktem|Eric Paquin|Grace Tang|Sylwia Tur,,38939854, +WS-26,WS-26.53,Public Sentiment on Governmental COVID-19 Measures in Dutch Social Media,Shihan Wang|Marijn Schraagen|Erik Tjong Kim Sang|Mehdi Dastani,,38939855, +WS-26,WS-26.6,Answering Questions on COVID-19 in Real-Time,Jinhyuk Lee|Sean S. Yi|Minbyul Jeong|Mujeen Sung|WonJin Yoon|Yonghwa Choi|Miyoung Ko|Jaewoo Kang,,38939843, +WS-26,WS-26.60,COVID-19 Literature Topic-Based Search via Hierarchical NMF,Rachel Grotheer|Longxiu Huang|Yihuan Huang|Alona Kryshchenko|Oleksandr Kryshchenko|Pengyu Li|Xia Li|Elizaveta Rebrova|Kyung Ha|Deanna Needell,,38939856, +WS-26,WS-26.63,Improved Topic Representations of Medical Documents to Assist COVID-19 Literature Exploration,Yulia Otmakhova|Karin Verspoor|Timothy Baldwin|Simon Suster,,38939857, +WS-26,WS-26.64,Automatic Evaluation vs. User Preference in Neural Textual QuestionAnswering over COVID-19 Scientific Literature,Arantxa Otegi|Jon Ander Campos|Gorka Azkune|Aitor Soroa|Eneko Agirre,,38939858, +WS-26,WS-26.65,Twitter Data Augmentation for Monitoring Public Opinion on COVID-19 Intervention Measures,Lin Miao|Mark Last|Marina Litvak,,38939859, +WS-26,WS-26.9,CAiRE-COVID: A Question Answering and Query-focused Multi-Document Summarization System for COVID-19 Scholarly Information Management,Dan Su|Yan Xu|Tiezheng Yu|Farhad Bin Siddique|Elham Barezi|Pascale Fung,,38939844, +WS-3,WS-3.1,Domain adaptation challenges of BERT in tokenization and sub-word representations of Out-of-Vocabulary words,Anmol Nayak|Hariprasad Timmapathini|Karthikeyan Ponnalagu|Vijendran Gopalan Venkoparao,,38940788,https://www.aclweb.org/anthology/2020.insights-1.1 +WS-3,WS-3.12,On Task-Level Dialogue Composition of Generative Transformer Model,Prasanna Parthasarathi|Sharan Narang|Arvind Neelakantan,,38940793,https://www.aclweb.org/anthology/2020.insights-1.6 +WS-3,WS-3.19,How Effectively Can Machines Defend Against Machine-Generated Fake News? An Empirical Study,Meghana Moorthy Bhat|Srinivasan Parthasarathy,,38940794,https://www.aclweb.org/anthology/2020.insights-1.7 +WS-3,WS-3.20,Label Propagation-Based Semi-Supervised Learning for Hate Speech Classification,Ashwin Geet D'Sa|Irina Illina|Dominique Fohr|Dietrich Klakow,,38940795,https://www.aclweb.org/anthology/2020.insights-1.8 +WS-3,WS-3.22,Layout-Aware Text Representations Harm Clustering Documents by Type,Catherine Finegan-Dollak|Ashish Verma,,38940796,https://www.aclweb.org/anthology/2020.insights-1.9 +WS-3,WS-3.23,An Analysis of Capsule Networks for Part of Speech Tagging in High- and Low-resource Scenarios,Andrew Zupon|Faiz Rafique|Mihai Surdeanu,,38940797,https://www.aclweb.org/anthology/2020.insights-1.10 +WS-3,WS-3.24,Can Knowledge Graph Embeddings Tell Us What Fact-checked Claims Are About?,Valentina Beretta|Sébastien Harispe|Katarina Boland|Luke Lo Seen|Konstantin Todorov|On Tchechmedjiev,,38940798,https://www.aclweb.org/anthology/2020.insights-1.11 +WS-3,WS-3.2436,WER we are and WER we think we are,Piotr Szymański|Piotr Żelasko|Mikolaj Morzy|Adrian Szymczak|Marzena Żyła-Hoppe|Joanna Banaszczak|Lukasz Augustyniak|Jan Mizgajski|Yishay Carmiel,,38940634,https://www.aclweb.org/anthology/2020.findings-emnlp.295 +WS-3,WS-3.2575-ws3,What do we expect from Multiple-choice QA Systems?,Krunal Shah|Nitish Gupta|Dan Roth,,38940809,https://www.aclweb.org/anthology/2020.findings-emnlp.317 +WS-3,WS-3.26,"Do Transformers Dream of Inference, or Can Pretrained Generative Models Learn Implicit Inferential Rules?",Zhengzhong Liang|Mihai Surdeanu,,38940799,https://www.aclweb.org/anthology/2020.insights-1.12 +WS-3,WS-3.27,Counterfactually-Augmented SNLI Training Data Does Not Yield Better Generalization Than Unaugmented Data,William Huang|Haokun Liu|Samuel R. Bowman,,38940800,https://www.aclweb.org/anthology/2020.insights-1.13 +WS-3,WS-3.29,NMF Ensembles? Not for Text Summarization!,Alka Khurana|Vasudha Bhatnagar,,38940801,https://www.aclweb.org/anthology/2020.insights-1.14 +WS-3,WS-3.30,"If You Build Your Own NER Scorer, Non-replicable Results Will Come",Constantine Lignos|Marjan Kamyab,,38940802,https://www.aclweb.org/anthology/2020.insights-1.15 +WS-3,WS-3.3004,Effects of Naturalistic Variation in Goal-Oriented Dialog,Jatin Ganhotra|Robert Moore|Sachindra Joshi|Kahini Wadhawan,,38940807,https://www.aclweb.org/anthology/2020.findings-emnlp.358 +WS-3,WS-3.31,HINT3: Raising the bar for Intent Detection in the Wild,Gaurav Arora|Chirag Jain|Manas Chaturvedi|Krupal Modi,,38940803,https://www.aclweb.org/anthology/2020.insights-1.16 +WS-3,WS-3.3106,Poison Attacks against Text Datasets with Conditional Adversarially Regularized Autoencoder,Alvin Chan|Yi Tay|Yew-Soon Ong|Aston Zhang,"This paper demonstrates a fatal vulnerability in natural language inference (NLI) and text classification systems. More concretely, we present a ‘backdoor poisoning’ attack on NLP models. Our poisoning attack utilizes conditional adversarially regularized autoencoder (CARA) to generate poisoned training samples by poison injection in latent space. Just by adding 1% poisoned data, our experiments show that a victim BERT finetuned classifier’s predictions can be steered to the poison target class with success rates of >80\% when the input hypothesis is injected with the poison signature, demonstrating that NLI and text classification systems face a huge security risk.",38940808,https://www.aclweb.org/anthology/2020.findings-emnlp.373 +WS-3,WS-3.3296,How Effective is Task-Agnostic Data Augmentation for Pretrained Transformers?,Shayne Longpre|Yu Wang|Christopher DuBois,,38940806,https://www.aclweb.org/anthology/2020.findings-emnlp.394 +WS-3,WS-3.33,The Extraordinary Failure of Complement Coercion Crowdsourcing,Yanai Elazar|Victoria Basmov|Shauli Ravfogel|Yoav Goldberg|Reut Tsarfat,,38940804,https://www.aclweb.org/anthology/2020.insights-1.17 +WS-3,WS-3.34,Embedding Structured Dictionary Entries,Steven Wilson|Walid Magdy|Barbara McGillivray|Gareth Tyson,,38940805,https://www.aclweb.org/anthology/2020.insights-1.18 +WS-3,WS-3.4,Q. Can Knowledge Graphs be used to Answer Boolean Questions? A. It's complicated!,Daria Dzendzik|Carl Vogel|Jennifer Foster,,38940789,https://www.aclweb.org/anthology/2020.insights-1.2 +WS-3,WS-3.6,How Far Can We Go with Data Selection? A Case Study on Semantic Sequence Tagging Tasks,Samuel Louvan|Bernardo Magnini,,38940790,https://www.aclweb.org/anthology/2020.insights-1.3 +WS-3,WS-3.7,Evaluating the Effectiveness of Efficient Neural Architecture Search for Sentence-Pair Tasks,Ansel MacLaughlin|Jwala Dhamala|Anoop Kumar|Sriram Venkatapathy|Ragav Venkatesan|Rahul Gupta,,38940791,https://www.aclweb.org/anthology/2020.insights-1.4 +WS-3,WS-3.8,Which Matters Most? Comparing the Impact of Concept and Document Relationships in Topic Models,Silvia Terragni|Debora Nozza|Elisabetta Fersini|Messina Enza,,38940792,https://www.aclweb.org/anthology/2020.insights-1.5 +WS-4,WS-4.1175,Filtering before Iteratively Referring for Knowledge-Grounded Response Selection in Retrieval-Based Chatbots,Jia-Chen Gu,,38940705,https://www.aclweb.org/anthology/2020.findings-emnlp.127 +WS-4,WS-4.1735,TSDG: Content-aware Neural Response Generation with Two-stage Decoding Process,Junsheng Kong|Zhicheng Zhong|Yi Cai|Xin Wu|Da Ren,,38940706,https://www.aclweb.org/anthology/2020.findings-emnlp.192 +WS-4,WS-4.1761-ws4,ConveRT: Efficient and Accurate Conversational Representations from Transformers,Ivan Vulić,,38940707,https://www.aclweb.org/anthology/2020.findings-emnlp.196 +WS-4,WS-4.1947,Learning Improvised Chatbots from Adversarial Modifications of Natural Language Feedback,Makesh Narsimhan Sreedhar|Kun Ni|Siva Reddy,,38940708,https://www.aclweb.org/anthology/2020.findings-emnlp.221 +WS-4,WS-4.2041,Toward Stance-based Personas for Opinionated Dialogues,Thomas Scialom|Serra Sinem Tekiroglu|Jacopo Staiano|Marco Guerini,,38940704,https://www.aclweb.org/anthology/2020.findings-emnlp.238 +WS-4,WS-4.2889,Joint Turn and Dialogue level User Satisfaction Estimation on Multi-Domain Conversations,Praveen Kumar Bodigutla|Aditya Tiwari|Josep Vallas Vargas|Lazaros Polymenakos|Spyros Matsoukas,,38940709,https://www.aclweb.org/anthology/2020.findings-emnlp.347 +WS-4,WS-4.2957,Making Information Seeking Easier: An Improved Pipeline for Conversational Search,Vaibhav Kumar|Jamie Callan,,38940710,https://www.aclweb.org/anthology/2020.findings-emnlp.354 +WS-4,WS-4.3361,SMRT Chatbots: Improving Non-Task-Oriented Dialog with Simulated Multi-Reference Training,Huda Khayrallah|João Sedoc,,38940711,https://www.aclweb.org/anthology/2020.findings-emnlp.403 +WS-4,WS-4.paper1,TBA,Tba,,38940061, +WS-4,WS-4.paper2,A Wrong Answer or a Wrong Question? An Intricate Relationship between Question Reformulation and Answer Selection in Conversational Question Answering,Svitlana Vakulenko|Shayne Longpre|Zhucheng Tu|Raviteja Anantha,,38940062,https://www.aclweb.org/anthology/2020.scai-1.2 +WS-4,WS-4.paper3,Semantically Driven Sentence Fusion: Modeling and Evaluation,Eyal Ben-David,,38940063,https://www.aclweb.org/anthology/2020.findings-emnlp.135 +WS-4,WS-4.paper4,TBA,Tba,,38940064, +WS-4,WS-4.paper5,TBA,Marco Guerini,,38940065, +WS-5,WS-5.16,Probabilistic Predictions of People Perusing: Evaluating Metrics of Language Model Performance for Psycholinguistic Modeling,Yiding Hao|Simon Mendelsohn|Rachel Sterneck|Randi Martinez|Robert Frank,,38939682,https://www.aclweb.org/anthology/2020.cmcl-1.10 +WS-5,WS-5.7,Images and Imagination: Automated Analysis of Priming Effects Related to Autism Spectrum Disorder and Developmental Language Disorder,Michaela Regneri|Diane King|Fahreen Walji|Olympia Palikara,,38939683,https://www.aclweb.org/anthology/2020.cmcl-1.2 +WS-6,WS-6.10,Learning Adaptive Language Interfaces through Decomposition,Siddharth Karamcheti|Dorsa Sadigh|Percy Liang,,38939456,https://www.aclweb.org/anthology/2020.intexsempar-1.4 +WS-6,WS-6.11,ColloQL: Robust Text-to-SQL Over Search Queries,Karthik Radhakrishnan|Arvind Srikantan|Xi Victoria Lin,,38939457,https://www.aclweb.org/anthology/2020.intexsempar-1.5 +WS-6,WS-6.12,Natural Language Response Generation from SQL with Generalization and Back-translation,Saptarashmi Bandyopadhyay|Tianyang Zhao,,38939458,https://www.aclweb.org/anthology/2020.intexsempar-1.6 +WS-6,WS-6.13,GRAPPA: Grammar-Augmented Pre-Training for Table Semantic Parsing,Tao Yu|Chien-Sheng Wu|Xi Victoria Lin|Bailin Wang|Yi Chern Tan|Xinyi Yang|Dragomir Radev|Richard Socher|Caiming Xiong,,38939459, +WS-6,WS-6.14,Re-thinking Open-domain Semantic Parsing,Yu Gu|Sue Kase|Michelle Vanni|Brian Sadler|Percy Liang|Xifeng Yan|Yu Su,,38939460, +WS-6,WS-6.15,Did You Ask a Good Question? A Cross-Domain Question Intention Classification Benchmark for Text-to-SQL,Yusen Zhang|Xiangyu Dong|Shuaichen Chang|Tao Yu|Peng Shi|Rui Zhang,,38939461, +WS-6,WS-6.7,QA2Explanation: Generating and Evaluating Explanations for Question Answering Systems over Knowledge Graph,Saeedeh Shekarpour|Abhishek Nadgeri|Kuldeep Singh,,38939453,https://www.aclweb.org/anthology/2020.intexsempar-1.1 +WS-6,WS-6.8,Uncertainty and Traffic-Aware Active Learning for Semantic Parsing,Priyanka Sen|Emine Yilmaz,,38939454,https://www.aclweb.org/anthology/2020.intexsempar-1.2 +WS-6,WS-6.9,Improving Sequence-to-Sequence Semantic Parser for Task Oriented Dialog,Chaoting Xuan,,38939455,https://www.aclweb.org/anthology/2020.intexsempar-1.3 +WS-7,WS-7.14,Improved Local Citation Recommendation Based on Context Enhanced with Global Information,Zoran Medić|Jan Snajder,,38940720,https://www.aclweb.org/anthology/2020.sdp-1.11 +WS-7,WS-7.1457,Will this Idea Spread Beyond Academia? Understanding Knowledge Transfer of Scientific Concepts across Text Corpora,Hancheng Cao|Mengjie Cheng|Zhepeng Cen|Daniel A. McFarland|Xiang Ren,,38940721,https://www.aclweb.org/anthology/2020.findings-emnlp.158 +WS-7,WS-7.15shared,"CIST@CL-SciSumm 2020, LongSumm 2020: Automatic Scientific Document Summarization",Lei Li|Yang Xie|Wei Liu|Yinan Liu|Yafei Jiang|Siya Qi|Xingyuan Li,,38940743,https://www.aclweb.org/anthology/2020.sdp-1.25 +WS-7,WS-7.17,SciWING– A Software Toolkit for Scientific Document Processing,Abhinav Ramesh Kashyap|Min-Yen Kan,,38940731,https://www.aclweb.org/anthology/2020.sdp-1.13 +WS-7,WS-7.1728,"MedICaT: A Dataset of Medical Images, Captions, and Textual References",Sanjay Subramanian|Lucy Lu Wang|Sachin Mehta|Ben Bogin|Madeleine Van Zuylen|Sravanthi Parasar|Sameer Singh|Matt Gardner|Hannaneh Hajishirzi,,38940723,https://www.aclweb.org/anthology/2020.findings-emnlp.191 +WS-7,WS-7.18,NLP-PINGAN-TECH @ CL-SciSumm 2020,Ling Chai|Guizhen Fu|Yuan Ni,,38941223,https://www.aclweb.org/anthology/2020.sdp-1.26 +WS-7,WS-7.20shared,Dimsum @LaySumm 20,Tiezheng Yu|Dan Su|Wenliang Dai|Pascale Fung,,38940741,https://www.aclweb.org/anthology/2020.sdp-1.35 +WS-7,WS-7.21,Multi-task Peer-Review Score Prediction,Jiyi Li|Ayaka Sato|Kazuya Shimura|Fumiyo Fukumoto,,38940727,https://www.aclweb.org/anthology/2020.sdp-1.14 +WS-7,WS-7.22,ERLKG: Entity Representation Learning and Knowledge Graph based association analysis of COVID-19 through mining of unstructured biomedical corpora,Sayantan Basu|Sinchani Chakraborty|Atif Hassan|Sana Siddique|Ashish Anand,,38940725,https://www.aclweb.org/anthology/2020.sdp-1.15 +WS-7,WS-7.24,Towards Grounding of Formulae,Takuto Asakura|ré Greiner-Petter|Akiko Aizawa|Yusuke Miyao,,38940733,https://www.aclweb.org/anthology/2020.sdp-1.16 +WS-7,WS-7.2502,Literature Retrieval for Precision Medicine with Neural Matching and Faceted Summarization,Jiho Noh|Ramakanth Kavuluru,,38940722,https://www.aclweb.org/anthology/2020.findings-emnlp.304 +WS-7,WS-7.26,Learning CNF Blocking for Large-scale Author Name Disambiguation,Kunho Kim|Athar Sefid|C Lee Giles,,38940717,https://www.aclweb.org/anthology/2020.sdp-1.8 +WS-7,WS-7.27,SChuBERT: Scholarly Document Chunks with BERT-encoding boost Citation Count Prediction,Thomas Van Dongen|Gideon Maillette De Buy Wenniger|Lambert Schomaker,,38940730,https://www.aclweb.org/anthology/2020.sdp-1.17 +WS-7,WS-7.29,Structure-Tags Improve Text Classification for Scholarly Document Quality Prediction,Gideon Maillette De Buy Wenniger|Thomas Van Dongen|Eleri Aedmaa|Herbert Teun Kruitbosch|Edwin A. Valentijn|Lambert Schomaker,,38940732,https://www.aclweb.org/anthology/2020.sdp-1.18 +WS-7,WS-7.30,Cydex: Neural Search Infrastructure for the Scholarly Literature,Shane Ding|Edwin Zhang|Jimmy Lin,,38940734,https://www.aclweb.org/anthology/2020.sdp-1.19 +WS-7,WS-7.32,A Smart System to Generate and Validate Question Answer Pairs for COVID-19 Literature,Rohan Bhambhoria|Luna Feng|Dawn Sepehr|John Chen|Conner Cowling|Sedef Kocak|Elham Dolatabadi,,38940713,https://www.aclweb.org/anthology/2020.sdp-1.4 +WS-7,WS-7.35,On the Use of Web Search to Improve Scientific Collections,Krutarth Patel|Cornelia Caragea|Sujatha Das Gollapalli,,38940728,https://www.aclweb.org/anthology/2020.sdp-1.20 +WS-7,WS-7.36,Scaling Systematic Literature Reviews with Machine Learning Pipelines,Seraphina Goldfarb-Tarrant|Alexander Robertson|Louise Donnison|Jasmina Lazic|Theodora Tsouloufi|Karen Smyth,,38940729,https://www.aclweb.org/anthology/2020.sdp-1.21 +WS-7,WS-7.37shared,Using Pre-Trained Transformer for a better Lay Summarization,Seungwon Kim,,38940740,https://www.aclweb.org/anthology/2020.sdp-1.38 +WS-7,WS-7.39,Acknowledgement Entity Recognition in CORD-19 Papers,Jian Wu|Pei Wang|Xin Wei|Sarah Rajtmajer|C Lee Giles|Christopher Griffin,,38940712,https://www.aclweb.org/anthology/2020.sdp-1.3 +WS-7,WS-7.40,DeepPaperComposer: A Simple Solution for Training Data Preparation for Parsing Research Papers,Meng Ling|Jian Chen,,38940719,https://www.aclweb.org/anthology/2020.sdp-1.10 +WS-7,WS-7.42,"Document-Level Definition Detection in Scholarly Documents: Existing Models, Error Analyses, and Future Directions",Dongyeop Kang|Rew Head|Risham Sidhu|Kyle Lo|Daniel Weld|Marti A. Hearst,,38940724,https://www.aclweb.org/anthology/2020.sdp-1.22 +WS-7,WS-7.43shared,"IIITBH-IITP@CL-SciSumm20, CL-LaySumm20, LongSumm20",Saichethan Miriyala Reddy|Naveen Saini|Sriparna Saha|Pushpak Bhattacharyya,,38940739,https://www.aclweb.org/anthology/2020.sdp-1.27 +WS-7,WS-7.44,Reconstructing Manual Information Extraction with DB-to-Document Backprojection: Experiments in the Life Science Domain,Mark-Christoph Muller|Sucheta Ghosh|Maja Rey|Ulrike Wittig|Wolfgang Muller|Michael Strube,,38940718,https://www.aclweb.org/anthology/2020.sdp-1.9 +WS-7,WS-7.45,"AUTH @ CLSciSumm 20, LaySumm 20, LongSumm 20",Alexios Gidiotis|Stefanos Stefanidis|Grigorios Tsoumakas,,38941222,https://www.aclweb.org/anthology/2020.sdp-1.28 +WS-7,WS-7.48_2shared,"SUMMAFORMERS @ LAYSUMM 20, LONGSUMM 20",Sayar Ghosh Roy|Nikhil Pinnaparaju|Risubh Jain|Manish Gupta|Vasudeva Varma,,38940742,https://www.aclweb.org/anthology/2020.sdp-1.39 +WS-7,WS-7.48shared,Scientific Document Summarization for LaySumm '20 and LongSumm '2,Sayar Ghosh Roy|Nikhil Pinnaparaju|Risubh Jain|Manish Gupta∗|Vasudeva Varma,,38940738, +WS-7,WS-7.49,UniHD@CL-SciSumm 2020: Citation Extraction as Search,Dennis Aumiller|Satya Almasian|Philip Hausner|Michael Gertz,,38941224,https://www.aclweb.org/anthology/2020.sdp-1.29 +WS-7,WS-7.5,"On the effectiveness of small, discriminatively pre-trained language representation models for biomedical text mining",Ibrahim Burak Ozyurt,,38940735,https://www.aclweb.org/anthology/2020.sdp-1.12 +WS-7,WS-7.54,CMU: CiteQA@CLSciSumm 2020,Anjana Umapathy|Karthik Radhakrishnan|Kinjal Jain|Rahul Singh,,38941225,https://www.aclweb.org/anthology/2020.sdp-1.34 +WS-7,WS-7.56shared,GUIR @ LongSumm 2020: Learning to Generate Long Summaries from Scientific Documents,Sajad Sotudeh Gharebagh|Arman Cohan|Nazli Goharian,,38940737,https://www.aclweb.org/anthology/2020.sdp-1.41 +WS-7,WS-7.6,The impact of preprint servers in the formation of novel ideas,Swarup Satish|Zonghai Yao|Rew Drozdov|Boris Veytsman,,38940715,https://www.aclweb.org/anthology/2020.sdp-1.6 +WS-7,WS-7.60,Covidex: Neural Ranking Models and Keyword Search Infrastructure for the COVID-19 Open Research Dataset.,Edwin Zhang|Nikhil Gupta|Raphael Tang|Xiao Han|Ronak Pradeep|Kuang Lu|Yue Zhang|Rodrigo Nogueira|Kyunghyun Cho|Hui Fang|Jimmy Lin,,38940714,https://www.aclweb.org/anthology/2020.sdp-1.5 +WS-7,WS-7.61,A New Neural Search and Insights Platform for Navigating and Organizing AI Research,Marzieh Fadaee|Olga Gureenkova|Fernando Rejon Barrera|Carsten Schnober|Wouter Weerkamp|Jakub Zavrel,,38940726,https://www.aclweb.org/anthology/2020.sdp-1.23 +WS-7,WS-7.758,Where to Submit? Helping Researchers to Choose the Right Venue,Konstantin Kobs|Tobias Koopmann|Albin Zehe|David Fernes|Philipp Krop|Reas Hotho,,38940736,https://www.aclweb.org/anthology/2020.findings-emnlp.78 +WS-7,WS-7.8,Effective Distributed Representations for Academic Expert Search,Mark Berger|Jakub Zavrel|Paul Groth,,38940716,https://www.aclweb.org/anthology/2020.sdp-1.7 +WS-9,WS-9.10,"fugashi, a Tool for Tokenizing Japanese in Python",Paul McCann,,38939744,https://www.aclweb.org/anthology/2020.nlposs-1.7 +WS-9,WS-9.11,Going Beyond T-SNE: Exposing whatlies in Text Embeddings,Vincent Warmerdam|Thomas Kober|Rachael Tatman,,38939745,https://www.aclweb.org/anthology/2020.nlposs-1.8 +WS-9,WS-9.12,"Howl: A Deployed, Open-Source Wake Word Detection System",Raphael Tang|Jaejun Lee|Afsaneh Razi|Julia Cambre|Ian Bicking|Jofish Kaye|Jimmy Lin,,38939746,https://www.aclweb.org/anthology/2020.nlposs-1.9 +WS-9,WS-9.13,iNLTK: Natural Language Toolkit for Indic Languages,Gaurav Arora,,38939747,https://www.aclweb.org/anthology/2020.nlposs-1.10 +WS-9,WS-9.14,iobes: A Library for Span-Level Processing,Brian Lester,,38939748,https://www.aclweb.org/anthology/2020.nlposs-1.16 +WS-9,WS-9.15,jiant: A Software Toolkit for Research on General-Purpose Text Understanding Models,Yada Pruksachatkun|Phil Yeres|Haokun Liu|Jason Phang|Phu Mon Htut|Alex Wang|Ian Tenney|Samuel R. Bowman,,38939749, +WS-9,WS-9.16,KLPT – Kurdish Language Processing Toolkit,Sina Ahmadi,,38939750,https://www.aclweb.org/anthology/2020.nlposs-1.11 +WS-9,WS-9.17,Open Korean Corpora: A Practical Report,Won Ik Cho|Sangwhan Moon|Youngsook Song,,38939751,https://www.aclweb.org/anthology/2020.nlposs-1.12 +WS-9,WS-9.18,Open-Source Morphology for Endangered Mordvinic Languages,Jack Rueter|Mika Hämäläinen|Niko Partanen,,38939752,https://www.aclweb.org/anthology/2020.nlposs-1.13 +WS-9,WS-9.19,Pimlico: A toolkit for corpus-processing pipelines and reproducible experiments,Mark Granroth-Wilding,,38939753,https://www.aclweb.org/anthology/2020.nlposs-1.14 +WS-9,WS-9.20,PySBD: Pragmatic Sentence Boundary Disambiguation,Nipun Sadvilkar|Mark Neumann,,38939754,https://www.aclweb.org/anthology/2020.nlposs-1.15 +WS-9,WS-9.21,SacreROUGE: An Open-Source Library for Using and Developing Summarization Evaluation Metrics,Daniel Deutsch|Dan Roth,,38939755,https://www.aclweb.org/anthology/2020.nlposs-1.17 +WS-9,WS-9.22,TextAttack: Lessons learned in designing Python frameworks for NLP,John Morris|Jin Yong Yoo|Yanjun Qi,,38939756,https://www.aclweb.org/anthology/2020.nlposs-1.18 +WS-9,WS-9.23,"TOMODAPI: A Topic Modeling API to Train, Use and Compare Topic Models",Pasquale Lisena|Ismail Harrando|Oussama Kandakji|Raphael Troncy,,38939757,https://www.aclweb.org/anthology/2020.nlposs-1.19 +WS-9,WS-9.24,User-centered Robust NLP OSS: Lessons Learned from Developing Maintaining RSMTool,Nitin Madnani|Anastassia Loukina,,38939758,https://www.aclweb.org/anthology/2020.nlposs-1.20 +WS-9,WS-9.25,WAFFLE: A Graph for WordNet Applied to FreeForm Linguistic Exploration,Berk Ekmekci|Blake Howald,,38939759,https://www.aclweb.org/anthology/2020.nlposs-1.21 +WS-9,WS-9.4,A Framework to Assist Chat Operators of Mental Healthcare Services,Thiago Madeira|Heder Bernardino|Jairo Francisco De Souza|Henrique Gomide|Nathália Munck Machado|Bruno Marcos Pinheiro Da Silva|Alexandre Vieira Pereira Pacelli,,38939738,https://www.aclweb.org/anthology/2020.nlposs-1.1 +WS-9,WS-9.5,ARBML: Democritizing Arabic Natural Language Processing Tools,Zaid Alyafeai|Maged Al-Shaibani,,38939739,https://www.aclweb.org/anthology/2020.nlposs-1.2 +WS-9,WS-9.6,CLEVR Parser: A Graph Parser Library for Geometric Learning on Language Grounded Image Scenes,Raeid Saqur|Ameet Deshpande,,38939740,https://www.aclweb.org/anthology/2020.nlposs-1.3 +WS-9,WS-9.7,End-to-end NLP Pipelines in Rust,Guillaume Becquin,,38939741,https://www.aclweb.org/anthology/2020.nlposs-1.4 +WS-9,WS-9.8,Fair Embedding Engine: A Library for Analyzing and Mitigating Gender Bias in Word Embeddings,Vaibhav Kumar|Tenzin Bhotia|Vaibhav Kumar,,38939742,https://www.aclweb.org/anthology/2020.nlposs-1.5 +WS-9,WS-9.9,Flexible retrieval with NMSLIB and FlexNeuART,Leonid Boytsov|Eric Nyberg,,38939743,https://www.aclweb.org/anthology/2020.nlposs-1.6 diff --git a/sitedata/workshops.yml b/sitedata/workshops.yml index 6bc1123..4a62ac6 100644 --- a/sitedata/workshops.yml +++ b/sitedata/workshops.yml @@ -501,7 +501,14 @@ hosts: TBD zoom_links: - https://us02web.zoom.us/j/8257686500?pwd=azc0bFltRk5ZQUp1TDhGRkxreGFiQT09 - prerecorded_talks: [] + prerecorded_talks: + - title: 'How Languages Carve Up the World: Cognitive Explanation through Computational + Modeling' + speakers: Suzanne Stevenson + presentation_id: '38939684' + - title: Information Processing, Communication, and Word Order + speakers: Richard Futrell + presentation_id: '38939685' - UID: WS-6 title: Interactive and Executable Semantic Parsing (Int-Ex) organizers: Ben Bogin, Srinivasan Iyer, Xi Victoria Lin, Panupong Pasupat, Alane @@ -1158,7 +1165,10 @@ zoom_links: - https://us02web.zoom.us/j/6468079734 - https://us02web.zoom.us/j/8739881077 - prerecorded_talks: [] + prerecorded_talks: + - title: Keynote 3rd Clinical Natural Language Processing Workshop + speakers: Hong Yu + presentation_id: '38939840' - UID: WS-13 title: 'Deep Learning Inside Out (DeeLIO): The First Workshop on Knowledge Extraction and Integration for Deep Learning Architectures' @@ -1213,7 +1223,14 @@ zoom_links: - https://us02web.zoom.us/j/9496849754 - https://us02web.zoom.us/j/4654314422 - prerecorded_talks: [] + prerecorded_talks: + - title: Keynote talk by Eduard Hovy + speakers: Eduard Hovy + presentation_id: '38940164' + - title: 'You can lead a horse to water...: Representing vs. Using Features in Neural + NLP' + speakers: Ellie Pavlick + presentation_id: '38940165' - UID: WS-14 title: 6th Workshop on Noisy User-generated Text (W-NUT 2020) organizers: Wei Xu, Alan Ritter, Timothy Baldwin, Afshin Rahimi and Leon Derczynski @@ -1565,7 +1582,16 @@ hosts: Vinodkumar Prabhakaran zoom_links: - https://us02web.zoom.us/j/2610080158 - prerecorded_talks: [] + prerecorded_talks: + - title: Keynote1 + speakers: "Andr\xE9 Brock" + presentation_id: '38939540' + - title: Keynote2 + speakers: Alex Hanna & Maliha Ahmed + presentation_id: '38939541' + - title: Keynote3 + speakers: Maria Y. Rodriguez + presentation_id: '38939542' - UID: WS-18 title: NLP and Computational Social Science (NLP+CSS) organizers: Svitlana Volkova, David Jurgens, David Bamman and Dirk Hovy diff --git a/templates/base.html b/templates/base.html index 334260f..feac2e8 100644 --- a/templates/base.html +++ b/templates/base.html @@ -89,7 +89,8 @@ ('workshops.html', 'Workshops'), ('socials.html', 'Socials'), ('sponsors.html', 'Sponsors'), - ('chat.html', 'Chat'), + ('http://' + config.chat_server, 'Chat'), + (config.gather_town , 'Gather'), ('organizers.html', 'Organizers'), ('about.html', 'Help'), ] -%} @@ -127,7 +128,11 @@
    {% for href, id in navigation_bar %}
  • + {% if href.startswith("http") %} + {{id}} + {% else %} {{id}} + {% endif %}
    • {% endfor %}
diff --git a/templates/index.html b/templates/index.html index 7902b14..9e28cce 100644 --- a/templates/index.html +++ b/templates/index.html @@ -65,6 +65,9 @@

The 7 tutorials and 25 workshops (November 19-20) are all included under conference registration. Some are fully pre-recorded, others include live sessions as well!
+
Gather.town
+
Join the virtual conference venue in Gather.Town! +
Sponsors
Meet our sponsors.
Chat
diff --git a/templates/paper.html b/templates/paper.html index 52a24b5..9c9b045 100644 --- a/templates/paper.html +++ b/templates/paper.html @@ -222,7 +222,7 @@

{% if config.connected_papers_conference_name and paper.content.program == "main" %}
{{ components.section("Connected Papers in EMNLP2020") }} -
From d83cf49212e98d56786f1ee52a1fa737e93848e8 Mon Sep 17 00:00:00 2001 From: Jan-Christoph Klie Date: Sun, 15 Nov 2020 02:24:21 +0100 Subject: [PATCH 3/5] No issue - Fix workshop papers --- miniconf/load_site_data.py | 8 +- scripts/dataentry/workshops.py | 6 +- sitedata/workshop_papers.csv | 738 ++++++++++++++++----------------- templates/paper.html | 3 + 4 files changed, 381 insertions(+), 374 deletions(-) diff --git a/miniconf/load_site_data.py b/miniconf/load_site_data.py index 019d18c..e715c18 100644 --- a/miniconf/load_site_data.py +++ b/miniconf/load_site_data.py @@ -790,10 +790,12 @@ def build_workshop_blocks(t: Dict[str, Any]) -> List[SessionInfo]: authors=extract_list_field(item, "authors"), track=workshop_title(workshop_id), paper_type="Workshop", - abstract=None, - tldr=None, + abstract=item.get("abstract"), + tldr=item["abstract"][:250] + "..." + if item["abstract"] + else None, keywords=[], - pdf_url=None, + pdf_url=item.get("pdf_url"), demo_url=None, sessions=[], similar_paper_uids=[], diff --git a/scripts/dataentry/workshops.py b/scripts/dataentry/workshops.py index 0d97e38..9278f3d 100644 --- a/scripts/dataentry/workshops.py +++ b/scripts/dataentry/workshops.py @@ -295,6 +295,8 @@ def generate_workshop_papers(slideslive: pd.DataFrame): uid_to_anthology_paper[uid] = author_to_anthology_paper[author.lower()] unmatched.remove((uid, title, author.lower())) + unmatched_df = pd.DataFrame(unmatched) + unmatched_df.to_csv("unmatched_workshop_papers.csv", index=False) for e in unmatched: print(e) @@ -399,7 +401,7 @@ def get_anthology_workshop_papers() -> List[Paper]: ] authors = "|".join(authors) - if paper.find("abstract"): + if paper.find("abstract") is not None: abstract = "".join(paper.find("abstract").itertext()) else: abstract = "" @@ -502,7 +504,7 @@ def get_zooms() -> Dict[str, List[str]]: uid = ws["UID"] ws["prerecorded_talks"] = talks[uid] - yaml.scalarstring.walk_tree(data) + yaml.scalarstring.walk_tree(data) with open(PATH_YAMLS / "workshops.yml", "w") as f: yaml.dump(data, f, Dumper=ruamel.yaml.RoundTripDumper) diff --git a/sitedata/workshop_papers.csv b/sitedata/workshop_papers.csv index 9a685e1..bbf8a69 100644 --- a/sitedata/workshop_papers.csv +++ b/sitedata/workshop_papers.csv @@ -1,322 +1,322 @@ workshop,UID,title,authors,abstract,presentation_id,pdf_url -WS-1,WS-1.100,Modelling Lexical Ambiguity with Density Matrices,Francois Meyer|Martha Lewis,,38939483,https://www.aclweb.org/anthology/2020.conll-1.21 -WS-1,WS-1.101,Catplayinginthesnow: Impact of Prior Segmentation on a Model of Visually Grounded Speech,William Havard|Laurent Besacier|Jean-Pierre Chevrot,,38939484,https://www.aclweb.org/anthology/2020.conll-1.22 -WS-1,WS-1.108,Learning to ground medical text in a 3D human atlas,Dusan Grujicic|Gorjan Radevski|Tinne Tuytelaars|Matthew Blaschko,,38939485,https://www.aclweb.org/anthology/2020.conll-1.23 -WS-1,WS-1.109,Representation Learning for Type-Driven Composition,Gijs Wijnholds|Mehrnoosh Sadrzadeh|Stephen Clark,,38939486,https://www.aclweb.org/anthology/2020.conll-1.24 +WS-1,WS-1.100,Modelling Lexical Ambiguity with Density Matrices,Francois Meyer|Martha Lewis,"Words can have multiple senses. Compositional distributional models of meaning have been argued to deal well with finer shades of meaning variation known as polysemy, but are not so well equipped to handle word senses that are etymologically unrelated, or homonymy. Moving from vectors to density matrices allows us to encode a probability distribution over different senses of a word, and can also be accommodated within a compositional distributional model of meaning. In this paper we present three new neural models for learning density matrices from a corpus, and test their ability to discriminate between word senses on a range of compositional datasets. When paired with a particular composition method, our best model outperforms existing vector-based compositional models as well as strong sentence encoders.",38939483,https://www.aclweb.org/anthology/2020.conll-1.21 +WS-1,WS-1.101,Catplayinginthesnow: Impact of Prior Segmentation on a Model of Visually Grounded Speech,William Havard|Laurent Besacier|Jean-Pierre Chevrot,"The language acquisition literature shows that children do not build their lexicon by segmenting the spoken input into phonemes and then building up words from them, but rather adopt a top-down approach and start by segmenting word-like units and then break them down into smaller units. This suggests that the ideal way of learning a language is by starting from full semantic units. In this paper, we investigate if this is also the case for a neural model of Visually Grounded Speech trained on a speech-image retrieval task. We evaluated how well such a network is able to learn a reliable speech-to-image mapping when provided with phone, syllable, or word boundary information. We present a simple way to introduce such information into an RNN-based model and investigate which type of boundary is the most efficient. We also explore at which level of the network’s architecture such information should be introduced so as to maximise its performances. Finally, we show that using multiple boundary types at once in a hierarchical structure, by which low-level segments are used to recompose high-level segments, is beneficial and yields better results than using low-level or high-level segments in isolation.",38939484,https://www.aclweb.org/anthology/2020.conll-1.22 +WS-1,WS-1.108,Learning to ground medical text in a 3D human atlas,Dusan Grujicic|Gorjan Radevski|Tinne Tuytelaars|Matthew Blaschko,"In this paper, we develop a method for grounding medical text into a physically meaningful and interpretable space corresponding to a human atlas. We build on text embedding architectures such as Bert and introduce a loss function that allows us to reason about the semantic and spatial relatedness of medical texts by learning a projection of the embedding into a 3D space representing the human body. We quantitatively and qualitatively demonstrate that our proposed method learns a context sensitive and spatially aware mapping, in both the inter-organ and intra-organ sense, using a large scale medical text dataset from the “Large-scale online biomedical semantic indexing” track of the 2020 BioASQ challenge. We extend our approach to a self-supervised setting, and find it to be competitive with a classification based method, and a fully supervised variant of approach.",38939485,https://www.aclweb.org/anthology/2020.conll-1.23 +WS-1,WS-1.109,Representation Learning for Type-Driven Composition,Gijs Wijnholds|Mehrnoosh Sadrzadeh|Stephen Clark,"This paper is about learning word representations using grammatical type information. We use the syntactic types of Combinatory Categorial Grammar to develop multilinear representations, i.e. maps with n arguments, for words with different functional types. The multilinear maps of words compose with each other to form sentence representations. We extend the skipgram algorithm from vectors to multi- linear maps to learn these representations and instantiate it on unary and binary maps for transitive verbs. These are evaluated on verb and sentence similarity and disambiguation tasks and a subset of the SICK relatedness dataset. Our model performs better than previous type- driven models and is competitive with state of the art representation learning methods such as BERT and neural sentence encoders.",38939486,https://www.aclweb.org/anthology/2020.conll-1.24 WS-1,WS-1.113,Word Representations Concentrate and This is Good News!,Romain Couillet|Yagmur Gizem Cinar|Eric Gaussier|Muhammad Imran,"This article establishes that, unlike the legacy tf*idf representation, recent natural language representations (word embedding vectors) tend to exhibit a so-called concentration of measure phenomenon, in the sense that, as the representation size p and database size n are both large, their behavior is similar to that of large dimensional Gaussian random vectors. This phenomenon may have important consequences as machine learning algorithms for natural language data could be amenable to improvement, thereby providing new theoretical insights into the field of natural language processing.",38939487,https://www.aclweb.org/anthology/2020.conll-1.25 -WS-1,WS-1.115,"""LazImpa"": Lazy and Impatient neural agents learn to communicate efficiently",Mathieu Rita|Rahma Chaabouni|Emmanuel Dupoux,,38939488,https://www.aclweb.org/anthology/2020.conll-1.26 -WS-1,WS-1.1165_F,Investigating Transferability in Pretrained Language Models,Alex Tamkin|Trisha Singh|Davide Giovanardi|Noah Goodman,,38940643,https://www.aclweb.org/anthology/2020.findings-emnlp.125 -WS-1,WS-1.127,Re-solve it: simulating the acquisition of core semantic competences from small data,Aurélie Herbelot,,38939489,https://www.aclweb.org/anthology/2020.conll-1.27 -WS-1,WS-1.128,In Media Res: A Corpus for Evaluating Named Entity Linking with Creative Works,Adrian M.P. Brasoveanu|Albert Weichselbraun|Lyndon Nixon,,38939490,https://www.aclweb.org/anthology/2020.conll-1.28 -WS-1,WS-1.136,Analogies minus analogy test: measuring regularities in word embeddings,Louis Fournier|Emmanuel Dupoux|Ewan Dunbar,,38939491,https://www.aclweb.org/anthology/2020.conll-1.29 -WS-1,WS-1.137,Word associations and the distance properties of context-aware word embeddings,Maria Andueza Rodriguez|Paola Merlo,,38939492,https://www.aclweb.org/anthology/2020.conll-1.30 -WS-1,WS-1.14,Neural Proof Nets,Konstantinos Kogkalidis|Michael Moortgat|Richard Moot,,38939465,https://www.aclweb.org/anthology/2020.conll-1.3 -WS-1,WS-1.142,TrClaim-19: The First Collection for Turkish Check-Worthy Claim Detection with Annotator Rationales,Yavuz Selim Kartal|Mucahid Kutlu,,38939493,https://www.aclweb.org/anthology/2020.conll-1.31 -WS-1,WS-1.144,Discourse structure interacts with reference but not syntax in neural language models,Forrest Davis|Marten Van Schijndel,,38939494,https://www.aclweb.org/anthology/2020.conll-1.32 -WS-1,WS-1.147,Continual Adaptation for Efficient Machine Communication,Robert Hawkins|Minae Kwon|Dorsa Sadigh|Noah Goodman,,38939495,https://www.aclweb.org/anthology/2020.conll-1.33 -WS-1,WS-1.149,Diverse and Relevant Visual Storytelling with Scene Graph Embeddings,Xudong Hong|Rakshith Shetty|Asad Sayeed|Khushboo Mehra|Vera Demberg|Bernt Schiele,,38939496,https://www.aclweb.org/anthology/2020.conll-1.34 -WS-1,WS-1.15,TaxiNLI: Taking a Ride up the NLU Hill,Pratik Joshi|Somak Aditya|Aalok Sathe|Monojit Choudhury,,38939466,https://www.aclweb.org/anthology/2020.conll-1.4 -WS-1,WS-1.152,Alleviating Digitization Errors in Named Entity Recognition for Historical Documents,Emanuela Boros|Ahmed Hamdi|Elvys Linhares Pontes|Luis Adrián Cabrera-Diego|Jose G. Moreno|Nicolas Sidere|Antoine Doucet,,38939497,https://www.aclweb.org/anthology/2020.conll-1.35 -WS-1,WS-1.155,Analysing Word Representation from the Input and Output Embeddings in Neural Network Language Models,Steven Derby|Paul Miller|Barry Devereux,,38939498,https://www.aclweb.org/anthology/2020.conll-1.36 -WS-1,WS-1.156,On the Computational Power of Transformers and Its Implications in Sequence Modeling,Satwik Bhattamishra|Arkil Patel|Navin Goyal,,38939499,https://www.aclweb.org/anthology/2020.conll-1.37 -WS-1,WS-1.1597_F,Pragmatic Issue-Sensitive Image Captioning,Allen Nie|Reuben Cohn-Gordon|Christopher Potts,,38940644,https://www.aclweb.org/anthology/2020.findings-emnlp.173 -WS-1,WS-1.162,An Expectation Maximisation Algorithm for Automated Cognate Detection,Roddy MacSween|Andrew Caines,,38939500,https://www.aclweb.org/anthology/2020.conll-1.38 -WS-1,WS-1.168,Filler-gaps that neural networks fail to generalize,Debasmita Bhattacharya|Marten Van Schijndel,,38939501,https://www.aclweb.org/anthology/2020.conll-1.39 -WS-1,WS-1.177,"Don't Parse, Insert: Multilingual Semantic Parsing with Insertion Based Decoding",Qile Zhu|Haidar Khan|Saleh Soltan|Stephen Rawls|Wael Hamza,,38939502,https://www.aclweb.org/anthology/2020.conll-1.40 -WS-1,WS-1.18,Modeling Subjective Assessments of Guilt in Newspaper Crime Narratives,Elisa Kreiss|Zijian Wang|Christopher Potts,,38939467,https://www.aclweb.org/anthology/2020.conll-1.5 -WS-1,WS-1.183,Learning Context-free Languages with Nondeterministic Stack RNNs,Brian DuSell|David Chiang,,38939503,https://www.aclweb.org/anthology/2020.conll-1.41 -WS-1,WS-1.185,Generating Narrative Text in a Switching Dynamical System,Noah Weber|Leena Shekhar|Heeyoung Kwon|Niranjan Balasubramanian|Nathanael Chambers,,38939504,https://www.aclweb.org/anthology/2020.conll-1.42 -WS-1,WS-1.189,What Are You Trying to Do? Semantic Typing of Event Processes,Muhao Chen|Hongming Zhang|Haoyu Wang|Dan Roth,,38939505,https://www.aclweb.org/anthology/2020.conll-1.43 -WS-1,WS-1.195,A Corpus for Outbreak Detection of Diseases Prevalent in Latin America,Antonella Dellanzo|Viviana Cotik|Jose Ochoa-Luna,,38939506,https://www.aclweb.org/anthology/2020.conll-1.44 -WS-1,WS-1.202,Are Pretrained Language Models Symbolic Reasoners over Knowledge?,Nora Kassner|Benno Krojer|Hinrich Schütze,,38939507,https://www.aclweb.org/anthology/2020.conll-1.45 -WS-1,WS-1.21,On the Frailty of Universal POS Tags for Neural UD Parsers,Mark Anderson|Carlos Gómez-Rodríguez,,38939468,https://www.aclweb.org/anthology/2020.conll-1.6 -WS-1,WS-1.218,Understanding Linguistic Accommodation in Code-Switched Human-Machine Dialogues,Tanmay Parekh|Emily Ahn|Yulia Tsvetkov|Alan W Black,,38939508,https://www.aclweb.org/anthology/2020.conll-1.46 -WS-1,WS-1.221,Identifying robust markers of Parkinson's disease in typing behaviour using a CNN-LSTM network,Neil Dhir|Mathias Edman|Álvaro Sanchez Ferro|Tom Stafford|Colin Bannard,,38939509,https://www.aclweb.org/anthology/2020.conll-1.47 -WS-1,WS-1.222,An Empirical Study on Model-agnostic Debiasing Strategies for Robust Natural Language Inference,Tianyu Liu|Zheng Xin|Xiaoan Ding|Baobao Chang|Zhifang Sui,,38939510,https://www.aclweb.org/anthology/2020.conll-1.48 -WS-1,WS-1.226,Cloze Distillation Improves Psychometric Predictive Power,Tiwalayo Eisape|Noga Zaslavsky|Roger Levy,,38939511,https://www.aclweb.org/anthology/2020.conll-1.49 -WS-1,WS-1.234,Disentangling dialects: a neural approach to Indo-Aryan historical phonology and subgrouping,Chundra Cathcart|Taraka Rama,,38939512,https://www.aclweb.org/anthology/2020.conll-1.50 -WS-1,WS-1.247,"A Dataset for Linguistic Understanding, Visual Evaluation, and Recognition of Sign Languages: The K-RSL",Alfarabi Imashev|Medet Mukushev|Vadim Kimmelman|Anara Sandygulova,,38939513,https://www.aclweb.org/anthology/2020.conll-1.51 -WS-1,WS-1.258,From Dataset Recycling to Multi-Property Extraction and Beyond,Tomasz Dwojak|Michał Pietruszka|Łukasz Borchmann|Jakub Chłędowski|Filip Graliński,,38939514,https://www.aclweb.org/anthology/2020.conll-1.52 -WS-1,WS-1.259,How well does surprisal explain N400 amplitude under different experimental conditions?,James Michaelov|Benjamin Bergen,,38939515,https://www.aclweb.org/anthology/2020.conll-1.53 -WS-1,WS-1.26,Classifying Syntactic Errors in Learner Language,Leshem Choshen|Dmitry Nikolaev|Yevgeni Berzak|Omri Abend,,38939469,https://www.aclweb.org/anthology/2020.conll-1.7 -WS-1,WS-1.28,How to Probe Sentence Embeddings in Low-Resource Languages: On Structural Design Choices for Probing Task Evaluation,Steffen Eger|Johannes Daxenberger|Iryna Gurevych,,38939470,https://www.aclweb.org/anthology/2020.conll-1.8 -WS-1,WS-1.29,Understanding the Source of Semantic Regularities in Word Embeddings,Hsiao-Yu Chiang|Jose Camacho-Collados|Zachary Pardos,,38939471,https://www.aclweb.org/anthology/2020.conll-1.9 -WS-1,WS-1.38,Finding The Right One and Resolving it,Payal Khullar|Arghya Bhattacharya|Manish Shrivastava,,38939472,https://www.aclweb.org/anthology/2020.conll-1.10 -WS-1,WS-1.49,Bridging Information-Seeking Human Gaze and Machine Reading Comprehension,Jonathan Malmaud|Roger Levy|Yevgeni Berzak,,38939473,https://www.aclweb.org/anthology/2020.conll-1.11 -WS-1,WS-1.510_F,Dynamic Data Selection for Curriculum Learning via Ability Estimation,John P. Lalor|Hong Yu,,38940641,https://www.aclweb.org/anthology/2020.findings-emnlp.48 -WS-1,WS-1.561_F,From Language to Language-ish: How Brain-Like is an LSTM's Representation of Nonsensical Language Stimuli?,Maryam Hashemzadeh|Greta Kaufeld|Martha White|Andrea E. Martin|Alona Fyshe,,38940642,https://www.aclweb.org/anthology/2020.findings-emnlp.57 -WS-1,WS-1.59,A Corpus of Very Short Scientific Summaries,Yifan Chen|Tamara Polajnar|Colin Batchelor|Simone Teufel,,38939474,https://www.aclweb.org/anthology/2020.conll-1.12 -WS-1,WS-1.61,Recurrent babbling: evaluating the acquisition of grammar from limited input data,Ludovica Pannitto|Aurélie Herbelot,,38939475,https://www.aclweb.org/anthology/2020.conll-1.13 -WS-1,WS-1.63,Explaining the efficiency of communication: How communicators can reduce their computational burden through interaction,Jacqueline Van Arkel|Marieke Woensdregt|Mark Dingemanse|Mark Blokpoel,,38939476,https://www.aclweb.org/anthology/2020.conll-1.14 -WS-1,WS-1.69,Acquiring language from speech by learning to remember and predict,Cory Shain|Micha Elsner,,38939477,https://www.aclweb.org/anthology/2020.conll-1.15 -WS-1,WS-1.69_F,"Stay Hungry, Stay Focused: Generating Informative and Specific Questions in Information-Seeking Conversations",Peng Qi|Yuhao Zhang|Christopher D. Manning,,38940640,https://www.aclweb.org/anthology/2020.findings-emnlp.3 -WS-1,WS-1.7,Enriching Word Embeddings with Temporal and Spatial Information,Hongyu Gong|Suma Bhat|Pramod Viswanath,,38939463,https://www.aclweb.org/anthology/2020.conll-1.1 -WS-1,WS-1.70,Identifying Incorrect Labels in the CoNLL-2003 Corpus,Frederick Reiss|Hong Xu|Bryan Cutler|Karthik Muthuraman|Zachary Eichenberger,,38939478,https://www.aclweb.org/anthology/2020.conll-1.16 -WS-1,WS-1.73,When is a bishop not like a rook? When it's like a rabbi! Multi-prototype BERT embeddings for estimating semantic relationships,Gabriella Chronis|Katrin Erk,,38939479,https://www.aclweb.org/anthology/2020.conll-1.17 -WS-1,WS-1.8,Interpreting Attention Models with Human Visual Attention in Machine Reading Comprehension,Ekta Sood|Simon Tannert|Diego Frassinelli|Andreas Bulling|Ngoc Thang Vu,,38939464,https://www.aclweb.org/anthology/2020.conll-1.2 -WS-1,WS-1.83,Processing effort is a poor predictor of cross-linguistic word order frequency,Brennan Gonering|Emily Morgan,,38939480,https://www.aclweb.org/anthology/2020.conll-1.18 -WS-1,WS-1.88,Relations between comprehensibility and adequacy errors in machine translation output,Maja Popović,,38939481,https://www.aclweb.org/anthology/2020.conll-1.19 -WS-1,WS-1.96,Cross-lingual Embeddings Reveal Universal and Lineage-Specific Patterns in Grammatical Gender Assignment,Hartger Veeman|Marc Allassonnière-Tang|Aleksandrs Berdicevskis|Ali Basirat,,38939482,https://www.aclweb.org/anthology/2020.conll-1.20 -WS-1,WS-1.Shared1,Hitachi at MRP 2020: Text-to-Graph-Notation Transducer,Hiroaki Ozaki|Gaku Morio|Yuta Koreeda|Terufumi Morishita|Toshinori Miyoshi,,38941228,https://www.aclweb.org/anthology/2020.conll-shared.4 -WS-1,WS-1.Shared2,HIT-SCIR at MRP 2020: Transition-based Parser and Iterative Inference Parser,Longxu Dou|Yunlong Feng|Yuqiu Ji|Wanxiang Che|Ting Liu,,38941229,https://www.aclweb.org/anthology/2020.conll-shared.6 -WS-1,WS-1.Shared3,HUJI-KU at MRP 2020: Two Transition-based Neural Parsers,Ofir Arviv|Ruixiang Cui|Daniel Hershcovich,,38941230,https://www.aclweb.org/anthology/2020.conll-shared.7 -WS-1,WS-1.Shared4,JBNU at MRP 2020: AMR Parsing Using a Joint State Model for Graph-Sequence Iterative Inference,Seung-Hoon Na|Jinwoo Min,,38941231,https://www.aclweb.org/anthology/2020.conll-shared.8 -WS-1,WS-1.Shared5,ÚFAL at MRP 2020: Permutation-invariant Semantic Parsing in PERIN,David Samuel|Milan Straka,,38941232,https://www.aclweb.org/anthology/2020.conll-shared.5 -WS-1,WS-1.Shared6,FGD at MRP 2020: Prague Tectogrammatical Graphs,Daniel Zeman|Jan Hajic,,38941233,https://www.aclweb.org/anthology/2020.conll-shared.3 -WS-1,WS-1.Shared7,DRS at MRP 2020: Dressing up Discourse Representation Structures as Graphs,Lasha Abzianidze|Johan Bos|Stephan Oepen,,38941234,https://www.aclweb.org/anthology/2020.conll-shared.2 +WS-1,WS-1.115,"""LazImpa"": Lazy and Impatient neural agents learn to communicate efficiently",Mathieu Rita|Rahma Chaabouni|Emmanuel Dupoux,"Previous work has shown that artificial neural agents naturally develop surprisingly non-efficient codes. This is illustrated by the fact that in a referential game involving a speaker and a listener neural networks optimizing accurate transmission over a discrete channel, the emergent messages fail to achieve an optimal length. Furthermore, frequent messages tend to be longer than infrequent ones, a pattern contrary to the Zipf Law of Abbreviation (ZLA) observed in all natural languages. Here, we show that near-optimal and ZLA-compatible messages can emerge, but only if both the speaker and the listener are modified. We hence introduce a new communication system, “LazImpa”, where the speaker is made increasingly lazy, i.e., avoids long messages, and the listener impatient, i.e., seeks to guess the intended content as soon as possible.",38939488,https://www.aclweb.org/anthology/2020.conll-1.26 +WS-1,WS-1.1165_F,Investigating Transferability in Pretrained Language Models,Alex Tamkin|Trisha Singh|Davide Giovanardi|Noah Goodman,"How does language model pretraining help transfer learning? We consider a simple ablation technique for determining the impact of each pretrained layer on transfer task performance. This method, partial reinitialization, involves replacing different layers of a pretrained model with random weights, then finetuning the entire model on the transfer task and observing the change in performance. This technique reveals that in BERT, layers with high probing performance on downstream GLUE tasks are neither necessary nor sufficient for high accuracy on those tasks. Furthermore, the benefit of using pretrained parameters for a layer varies dramatically with finetuning dataset size: parameters that provide tremendous performance improvement when data is plentiful may provide negligible benefits in data-scarce settings. These results reveal the complexity of the transfer learning process, highlighting the limitations of methods that operate on frozen models or single data samples.",38940643,https://www.aclweb.org/anthology/2020.findings-emnlp.125 +WS-1,WS-1.127,Re-solve it: simulating the acquisition of core semantic competences from small data,Aurélie Herbelot,"Many tasks are considered to be ‘solved’ in the computational linguistics literature, but the corresponding algorithms operate in ways which are radically different from human cognition. I illustrate this by coming back to the notion of semantic competence, which includes basic linguistic skills encompassing both referential phenomena and generic knowledge, in particular a) the ability to denote, b) the mastery of the lexicon, or c) the ability to model one’s language use on others. Even though each of those faculties has been extensively tested individually, there is still no computational model that would account for their joint acquisition under the conditions experienced by a human. In this paper, I focus on one particular aspect of this problem: the amount of linguistic data available to the child or machine. I show that given the first competence mentioned above (a denotation function), the other two can in fact be learned from very limited data (2.8M token), reaching state-of-the-art performance. I argue that both the nature of the data and the way it is presented to the system matter to acquisition.",38939489,https://www.aclweb.org/anthology/2020.conll-1.27 +WS-1,WS-1.128,In Media Res: A Corpus for Evaluating Named Entity Linking with Creative Works,Adrian M.P. Brasoveanu|Albert Weichselbraun|Lyndon Nixon,"Annotation styles express guidelines that direct human annotators in what rules to follow when creating gold standard annotations of text corpora. These guidelines not only shape the gold standards they help create, but also influence the training and evaluation of Named Entity Linking (NEL) tools, since different annotation styles correspond to divergent views on the entities present in the same texts. Such divergence is particularly present in texts from the media domain that contain references to creative works. In this work we present a corpus of 1000 annotated documents selected from the media domain. Each document is presented with multiple gold standard annotations representing various annotation styles. This corpus is used to evaluate a series of Named Entity Linking tools in order to understand the impact of the differences in annotation styles on the reported accuracy when processing highly ambiguous entities such as names of creative works. Relaxed annotation guidelines that include overlap styles lead to better results across all tools.",38939490,https://www.aclweb.org/anthology/2020.conll-1.28 +WS-1,WS-1.136,Analogies minus analogy test: measuring regularities in word embeddings,Louis Fournier|Emmanuel Dupoux|Ewan Dunbar,"Vector space models of words have long been claimed to capture linguistic regularities as simple vector translations, but problems have been raised with this claim. We decompose and empirically analyze the classic arithmetic word analogy test, to motivate two new metrics that address the issues with the standard test, and which distinguish between class-wise offset concentration (similar directions between pairs of words drawn from different broad classes, such as France-London, China-Ottawa,...) and pairing consistency (the existence of a regular transformation between correctly-matched pairs such as France:Paris::China:Beijing). We show that, while the standard analogy test is flawed, several popular word embeddings do nevertheless encode linguistic regularities.",38939491,https://www.aclweb.org/anthology/2020.conll-1.29 +WS-1,WS-1.137,Word associations and the distance properties of context-aware word embeddings,Maria Andueza Rodriguez|Paola Merlo,"What do people know when they know the meaning of words? Word associations have been widely used to tap into lexical repre- sentations and their structure, as a way of probing semantic knowledge in humans. We investigate whether current word embedding spaces (contextualized and uncontextualized) can be considered good models of human lexi- cal knowledge by studying whether they have comparable characteristics to human associa- tion spaces. We study the three properties of association rank, asymmetry of similarity and triangle inequality. We find that word embeddings are good mod- els of some word associations properties. They replicate well human associations between words, and, like humans, their context-aware variants show violations of the triangle in- equality. While they do show asymmetry of similarities, their asymmetries do not map those of human association norms.",38939492,https://www.aclweb.org/anthology/2020.conll-1.30 +WS-1,WS-1.14,Neural Proof Nets,Konstantinos Kogkalidis|Michael Moortgat|Richard Moot,"Linear logic and the linear λ-calculus have a long standing tradition in the study of natural language form and meaning. Among the proof calculi of linear logic, proof nets are of particular interest, offering an attractive geometric representation of derivations that is unburdened by the bureaucratic complications of conventional prooftheoretic formats. Building on recent advances in set-theoretic learning, we propose a neural variant of proof nets based on Sinkhorn networks, which allows us to translate parsing as the problem of extracting syntactic primitives and permuting them into alignment. Our methodology induces a batch-efficient, end-to-end differentiable architecture that actualizes a formally grounded yet highly efficient neuro-symbolic parser. We test our approach on ÆThel, a dataset of type-logical derivations for written Dutch, where it manages to correctly transcribe raw text sentences into proofs and terms of the linear λ-calculus with an accuracy of as high as 70%.",38939465,https://www.aclweb.org/anthology/2020.conll-1.3 +WS-1,WS-1.142,TrClaim-19: The First Collection for Turkish Check-Worthy Claim Detection with Annotator Rationales,Yavuz Selim Kartal|Mucahid Kutlu,"Massive misinformation spread over Internet has many negative impacts on our lives. While spreading a claim is easy, investigating its veracity is hard and time consuming, Therefore, we urgently need systems to help human fact-checkers. However, available data resources to develop effective systems are limited and the vast majority of them is for English. In this work, we introduce TrClaim-19, which is the very first labeled dataset for Turkish check-worthy claims. TrClaim-19 consists of labeled 2287 Turkish tweets with annotator rationales, enabling us to better understand the characteristics of check-worthy claims. The rationales we collected suggest that claims’ topics and their possible negative impacts are the main factors affecting their check-worthiness.",38939493,https://www.aclweb.org/anthology/2020.conll-1.31 +WS-1,WS-1.144,Discourse structure interacts with reference but not syntax in neural language models,Forrest Davis|Marten Van Schijndel,"Language models (LMs) trained on large quantities of text have been claimed to acquire abstract linguistic representations. Our work tests the robustness of these abstractions by focusing on the ability of LMs to learn interactions between different linguistic representations. In particular, we utilized stimuli from psycholinguistic studies showing that humans can condition reference (i.e. coreference resolution) and syntactic processing on the same discourse structure (implicit causality). We compared both transformer and long short-term memory LMs to find that, contrary to humans, implicit causality only influences LM behavior for reference, not syntax, despite model representations that encode the necessary discourse information. Our results further suggest that LM behavior can contradict not only learned representations of discourse but also syntactic agreement, pointing to shortcomings of standard language modeling.",38939494,https://www.aclweb.org/anthology/2020.conll-1.32 +WS-1,WS-1.147,Continual Adaptation for Efficient Machine Communication,Robert Hawkins|Minae Kwon|Dorsa Sadigh|Noah Goodman,"To communicate with new partners in new contexts, humans rapidly form new linguistic conventions. Recent neural language models are able to comprehend and produce the existing conventions present in their training data, but are not able to flexibly and interactively adapt those conventions on the fly as humans do. We introduce an interactive repeated reference task as a benchmark for models of adaptation in communication and propose a regularized continual learning framework that allows an artificial agent initialized with a generic language model to more accurately and efficiently communicate with a partner over time. We evaluate this framework through simulations on COCO and in real-time reference game experiments with human partners.",38939495,https://www.aclweb.org/anthology/2020.conll-1.33 +WS-1,WS-1.149,Diverse and Relevant Visual Storytelling with Scene Graph Embeddings,Xudong Hong|Rakshith Shetty|Asad Sayeed|Khushboo Mehra|Vera Demberg|Bernt Schiele,"A problem in automatically generated stories for image sequences is that they use overly generic vocabulary and phrase structure and fail to match the distributional characteristics of human-generated text. We address this problem by introducing explicit representations for objects and their relations by extracting scene graphs from the images. Utilizing an embedding of this scene graph enables our model to more explicitly reason over objects and their relations during story generation, compared to the global features from an object classifier used in previous work. We apply metrics that account for the diversity of words and phrases of generated stories as well as for reference to narratively-salient image features and show that our approach outperforms previous systems. Our experiments also indicate that our models obtain competitive results on reference-based metrics.",38939496,https://www.aclweb.org/anthology/2020.conll-1.34 +WS-1,WS-1.15,TaxiNLI: Taking a Ride up the NLU Hill,Pratik Joshi|Somak Aditya|Aalok Sathe|Monojit Choudhury,"Pre-trained Transformer-based neural architectures have consistently achieved state-of-the-art performance in the Natural Language Inference (NLI) task. Since NLI examples encompass a variety of linguistic, logical, and reasoning phenomena, it remains unclear as to which specific concepts are learnt by the trained systems and where they can achieve strong generalization. To investigate this question, we propose a taxonomic hierarchy of categories that are relevant for the NLI task. We introduce TaxiNLI, a new dataset, that has 10k examples from the MNLI dataset with these taxonomic labels. Through various experiments on TaxiNLI, we observe that whereas for certain taxonomic categories SOTA neural models have achieved near perfect accuracies—a large jump over the previous models—some categories still remain difficult. Our work adds to the growing body of literature that shows the gaps in the current NLI systems and datasets through a systematic presentation and analysis of reasoning categories.",38939466,https://www.aclweb.org/anthology/2020.conll-1.4 +WS-1,WS-1.152,Alleviating Digitization Errors in Named Entity Recognition for Historical Documents,Emanuela Boros|Ahmed Hamdi|Elvys Linhares Pontes|Luis Adrián Cabrera-Diego|Jose G. Moreno|Nicolas Sidere|Antoine Doucet,"This paper tackles the task of named entity recognition (NER) applied to digitized historical texts obtained from processing digital images of newspapers using optical character recognition (OCR) techniques. We argue that the main challenge for this task is that the OCR process leads to misspellings and linguistic errors in the output text. Moreover, historical variations can be present in aged documents, which can impact the performance of the NER process. We conduct a comparative evaluation on two historical datasets in German and French against previous state-of-the-art models, and we propose a model based on a hierarchical stack of Transformers to approach the NER task for historical data. Our findings show that the proposed model clearly improves the results on both historical datasets, and does not degrade the results for modern datasets.",38939497,https://www.aclweb.org/anthology/2020.conll-1.35 +WS-1,WS-1.155,Analysing Word Representation from the Input and Output Embeddings in Neural Network Language Models,Steven Derby|Paul Miller|Barry Devereux,"Researchers have recently demonstrated that tying the neural weights between the input look-up table and the output classification layer can improve training and lower perplexity on sequence learning tasks such as language modelling. Such a procedure is possible due to the design of the softmax classification layer, which previous work has shown to comprise a viable set of semantic representations for the model vocabulary, and these these output embeddings are known to perform well on word similarity benchmarks. In this paper, we make meaningful comparisons between the input and output embeddings and other SOTA distributional models to gain a better understanding of the types of information they represent. We also construct a new set of word embeddings using the output embeddings to create locally-optimal approximations for the intermediate representations from the language model. These locally-optimal embeddings demonstrate excellent performance across all our evaluations.",38939498,https://www.aclweb.org/anthology/2020.conll-1.36 +WS-1,WS-1.156,On the Computational Power of Transformers and Its Implications in Sequence Modeling,Satwik Bhattamishra|Arkil Patel|Navin Goyal,"Transformers are being used extensively across several sequence modeling tasks. Significant research effort has been devoted to experimentally probe the inner workings of Transformers. However, our conceptual and theoretical understanding of their power and inherent limitations is still nascent. In particular, the roles of various components in Transformers such as positional encodings, attention heads, residual connections, and feedforward networks, are not clear. In this paper, we take a step towards answering these questions. We analyze the computational power as captured by Turing-completeness. We first provide an alternate and simpler proof to show that vanilla Transformers are Turing-complete and then we prove that Transformers with only positional masking and without any positional encoding are also Turing-complete. We further analyze the necessity of each component for the Turing-completeness of the network; interestingly, we find that a particular type of residual connection is necessary. We demonstrate the practical implications of our results via experiments on machine translation and synthetic tasks.",38939499,https://www.aclweb.org/anthology/2020.conll-1.37 +WS-1,WS-1.1597_F,Pragmatic Issue-Sensitive Image Captioning,Allen Nie|Reuben Cohn-Gordon|Christopher Potts,"Image captioning systems need to produce texts that are not only true but also relevant in that they are properly aligned with the current issues. For instance, in a newspaper article about a sports event, a caption that not only identifies the player in a picture but also comments on their ethnicity could create unwanted reader reactions. To address this, we propose Issue-Sensitive Image Captioning (ISIC). In ISIC, the captioner is given a target image and an issue, which is a set of images partitioned in a way that specifies what information is relevant. For the sports article, we could construct a partition that places images into equivalence classes based on player position. To model this task, we use an extension of the Rational Speech Acts model. Our extension is built on top of state-of-the-art pretrained neural image captioners and explicitly uses image partitions to control caption generation. In both automatic and human evaluations, we show that these models generate captions that are descriptive and issue-sensitive. Finally, we show how ISIC can complement and enrich the related task of Visual Question Answering.",38940644,https://www.aclweb.org/anthology/2020.findings-emnlp.173 +WS-1,WS-1.162,An Expectation Maximisation Algorithm for Automated Cognate Detection,Roddy MacSween|Andrew Caines,"In historical linguistics, cognate detection is the task of determining whether sets of words have common etymological roots. Inspired by the comparative method used by human linguists, we develop a system for automated cognate detection that frames the task as an inference problem for a general statistical model consisting of observed data (potentially cognate pairs of words), latent variables (the cognacy status of pairs) and unknown global parameters (which sounds correspond between languages). We then give a specific instance of such a model along with an expectation-maximisation algorithm to infer its parameters. We evaluate our system on a corpus of 8140 cognate sets, finding the performance of our method to be comparable to the state of the art. We additionally carry out qualitative analysis demonstrating advantages it has over existing systems. We also suggest several ways our work could be extended within the general theoretical framework we propose.",38939500,https://www.aclweb.org/anthology/2020.conll-1.38 +WS-1,WS-1.168,Filler-gaps that neural networks fail to generalize,Debasmita Bhattacharya|Marten Van Schijndel,"It can be difficult to separate abstract linguistic knowledge in recurrent neural networks (RNNs) from surface heuristics. In this work, we probe for highly abstract syntactic constraints that have been claimed to govern the behavior of filler-gap dependencies across different surface constructions. For models to generalize abstract patterns in expected ways to unseen data, they must share representational features in predictable ways. We use cumulative priming to test for representational overlap between disparate filler-gap constructions in English and find evidence that the models learn a general representation for the existence of filler-gap dependencies. However, we find no evidence that the models learn any of the shared underlying grammatical constraints we tested. Our work raises questions about the degree to which RNN language models learn abstract linguistic representations.",38939501,https://www.aclweb.org/anthology/2020.conll-1.39 +WS-1,WS-1.177,"Don't Parse, Insert: Multilingual Semantic Parsing with Insertion Based Decoding",Qile Zhu|Haidar Khan|Saleh Soltan|Stephen Rawls|Wael Hamza,"Semantic parsing is one of the key components of natural language understanding systems. A successful parse transforms an input utterance to an action that is easily understood by the system. Many algorithms have been proposed to solve this problem, from conventional rule-based or statistical slot-filling systems to shift-reduce based neural parsers. For complex parsing tasks, the state-of-the-art method is based on an autoregressive sequence to sequence model that generates the parse directly. This model is slow at inference time, generating parses in O(n) decoding steps (n is the length of the target sequence). In addition, we demonstrate that this method performs poorly in zero-shot cross-lingual transfer learning settings. In this paper, we propose a non-autoregressive parser which is based on the insertion transformer to overcome these two issues. Our approach 1) speeds up decoding by 3x while outperforming the autoregressive model and 2) significantly improves cross-lingual transfer in the low-resource setting by 37% compared to autoregressive baseline. We test our approach on three wellknown monolingual datasets: ATIS, SNIPS and TOP. For cross-lingual semantic parsing, we use the MultiATIS++ and the multilingual TOP datasets.",38939502,https://www.aclweb.org/anthology/2020.conll-1.40 +WS-1,WS-1.18,Modeling Subjective Assessments of Guilt in Newspaper Crime Narratives,Elisa Kreiss|Zijian Wang|Christopher Potts,"Crime reporting is a prevalent form of journalism with the power to shape public perceptions and social policies. How does the language of these reports act on readers? We seek to address this question with the SuspectGuilt Corpus of annotated crime stories from English-language newspapers in the U.S. For SuspectGuilt, annotators read short crime articles and provided text-level ratings concerning the guilt of the main suspect as well as span-level annotations indicating which parts of the story they felt most influenced their ratings. SuspectGuilt thus provides a rich picture of how linguistic choices affect subjective guilt judgments. We use SuspectGuilt to train and assess predictive models which validate the usefulness of the corpus, and show that these models benefit from genre pretraining and joint supervision from the text-level ratings and span-level annotations. Such models might be used as tools for understanding the societal effects of crime reporting.",38939467,https://www.aclweb.org/anthology/2020.conll-1.5 +WS-1,WS-1.183,Learning Context-free Languages with Nondeterministic Stack RNNs,Brian DuSell|David Chiang,"We present a differentiable stack data structure that simultaneously and tractably encodes an exponential number of stack configurations, based on Lang’s algorithm for simulating nondeterministic pushdown automata. We call the combination of this data structure with a recurrent neural network (RNN) controller a Nondeterministic Stack RNN. We compare our model against existing stack RNNs on various formal languages, demonstrating that our model converges more reliably to algorithmic behavior on deterministic tasks, and achieves lower cross-entropy on inherently nondeterministic tasks.",38939503,https://www.aclweb.org/anthology/2020.conll-1.41 +WS-1,WS-1.185,Generating Narrative Text in a Switching Dynamical System,Noah Weber|Leena Shekhar|Heeyoung Kwon|Niranjan Balasubramanian|Nathanael Chambers,"Early work on narrative modeling used explicit plans and goals to generate stories, but the language generation itself was restricted and inflexible. Modern methods use language models for more robust generation, but often lack an explicit representation of the scaffolding and dynamics that guide a coherent narrative. This paper introduces a new model that integrates explicit narrative structure with neural language models, formalizing narrative modeling as a Switching Linear Dynamical System (SLDS). A SLDS is a dynamical system in which the latent dynamics of the system (i.e. how the state vector transforms over time) is controlled by top-level discrete switching variables. The switching variables represent narrative structure (e.g., sentiment or discourse states), while the latent state vector encodes information on the current state of the narrative. This probabilistic formulation allows us to control generation, and can be learned in a semi-supervised fashion using both labeled and unlabeled data. Additionally, we derive a Gibbs sampler for our model that can “fill in” arbitrary parts of the narrative, guided by the switching variables. Our filled-in (English language) narratives outperform several baselines on both automatic and human evaluations",38939504,https://www.aclweb.org/anthology/2020.conll-1.42 +WS-1,WS-1.189,What Are You Trying to Do? Semantic Typing of Event Processes,Muhao Chen|Hongming Zhang|Haoyu Wang|Dan Roth,"This paper studies a new cognitively motivated semantic typing task,multi-axis event process typing, that, given anevent process, attempts to infer free-form typelabels describing (i) the type of action made bythe process and (ii) the type of object the pro-cess seeks to affect. This task is inspired bycomputational and cognitive studies of eventunderstanding, which suggest that understand-ing processes of events is often directed by rec-ognizing the goals, plans or intentions of theprotagonist(s). We develop a large dataset con-taining over 60k event processes, featuring ul-tra fine-grained typing on both the action andobject type axes with very large (10ˆ3∼10ˆ4)label vocabularies. We then propose a hybridlearning framework,P2GT, which addressesthe challenging typing problem with indirectsupervision from glosses1and a joint learning-to-rank framework. As our experiments indi-cate,P2GTsupports identifying the intent ofprocesses, as well as the fine semantic type ofthe affected object. It also demonstrates the ca-pability of handling few-shot cases, and stronggeneralizability on out-of-domain processes.",38939505,https://www.aclweb.org/anthology/2020.conll-1.43 +WS-1,WS-1.195,A Corpus for Outbreak Detection of Diseases Prevalent in Latin America,Antonella Dellanzo|Viviana Cotik|Jose Ochoa-Luna,"In this paper we present an annotated corpus which can be used for training and testing algorithms to automatically extract information about diseases outbreaks from news and health reports. We also propose initial approaches to extract information from it. The corpus has been constructed with two main tasks in mind. The first one, to extract entities about outbreaks such as disease, host, location among others. The second one, to retrieve relations among entities, for instance, in such geographic location fifteen cases of a given disease were reported. Overall, our goal is to offer resources and tools to perform an automated analysis so as to support early detection of disease outbreaks and therefore diminish their spreading.",38939506,https://www.aclweb.org/anthology/2020.conll-1.44 +WS-1,WS-1.202,Are Pretrained Language Models Symbolic Reasoners over Knowledge?,Nora Kassner|Benno Krojer|Hinrich Schütze,"How can pretrained language models (PLMs) learn factual knowledge from the training set? We investigate the two most important mechanisms: reasoning and memorization. Prior work has attempted to quantify the number of facts PLMs learn, but we present, using synthetic data, the first study that investigates the causal relation between facts present in training and facts learned by the PLM. For reasoning, we show that PLMs seem to learn to apply some symbolic reasoning rules correctly but struggle with others, including two-hop reasoning. Further analysis suggests that even the application of learned reasoning rules is flawed. For memorization, we identify schema conformity (facts systematically supported by other facts) and frequency as key factors for its success.",38939507,https://www.aclweb.org/anthology/2020.conll-1.45 +WS-1,WS-1.21,On the Frailty of Universal POS Tags for Neural UD Parsers,Mark Anderson|Carlos Gómez-Rodríguez,"We present an analysis on the effect UPOS accuracy has on parsing performance. Results suggest that leveraging UPOS tags as fea-tures for neural parsers requires a prohibitively high tagging accuracy and that the use of gold tags offers a non-linear increase in performance, suggesting some sort of exceptionality. We also investigate what aspects of predicted UPOS tags impact parsing accuracy the most, highlighting some potentially meaningful linguistic facets of the problem.",38939468,https://www.aclweb.org/anthology/2020.conll-1.6 +WS-1,WS-1.218,Understanding Linguistic Accommodation in Code-Switched Human-Machine Dialogues,Tanmay Parekh|Emily Ahn|Yulia Tsvetkov|Alan W Black,"Code-switching is a ubiquitous phenomenon in multilingual communities. Natural language technologies that wish to communicate like humans must therefore adaptively incorporate code-switching techniques when they are deployed in multilingual settings. To this end, we propose a Hindi-English human-machine dialogue system that elicits code-switching conversations in a controlled setting. It uses different code-switching agent strategies to understand how users respond and accommodate to the agent’s language choice. Through this system, we collect and release a new dataset CommonDost, comprising of 439 human-machine multilingual conversations. We adapt pre-defined metrics to discover linguistic accommodation from users to agents. Finally, we compare these dialogues with Spanish-English dialogues collected in a similar setting, and analyze the impact of linguistic and socio-cultural factors on code-switching patterns across the two language pairs.",38939508,https://www.aclweb.org/anthology/2020.conll-1.46 +WS-1,WS-1.221,Identifying robust markers of Parkinson's disease in typing behaviour using a CNN-LSTM network,Neil Dhir|Mathias Edman|Álvaro Sanchez Ferro|Tom Stafford|Colin Bannard,"There is urgent need for non-intrusive tests that can detect early signs of Parkinson’s disease (PD), a debilitating neurodegenerative disorder that affects motor control. Recent promising research has focused on disease markers evident in the fine-motor behaviour of typing. Most work to date has focused solely on the timing of keypresses without reference to the linguistic content. In this paper we argue that the identity of the key combinations being produced should impact how they are handled by people with PD, and provide evidence that natural language processing methods can thus be of help in identifying signs of disease. We test the performance of a bi-directional LSTM with convolutional features in distinguishing people with PD from age-matched controls typing in English and Spanish, both in clinics and online.",38939509,https://www.aclweb.org/anthology/2020.conll-1.47 +WS-1,WS-1.222,An Empirical Study on Model-agnostic Debiasing Strategies for Robust Natural Language Inference,Tianyu Liu|Zheng Xin|Xiaoan Ding|Baobao Chang|Zhifang Sui,"The prior work on natural language inference (NLI) debiasing mainly targets at one or few known biases while not necessarily making the models more robust. In this paper, we focus on the model-agnostic debiasing strategies and explore how to (or is it possible to) make the NLI models robust to multiple distinct adversarial attacks while keeping or even strengthening the models’ generalization power. We firstly benchmark prevailing neural NLI models including pretrained ones on various adversarial datasets. We then try to combat distinct known biases by modifying a mixture of experts (MoE) ensemble method and show that it’s nontrivial to mitigate multiple NLI biases at the same time, and that model-level ensemble method outperforms MoE ensemble method. We also perform data augmentation including text swap, word substitution and paraphrase and prove its efficiency in combating various (though not all) adversarial attacks at the same time. Finally, we investigate several methods to merge heterogeneous training data (1.35M) and perform model ensembling, which are straightforward but effective to strengthen NLI models.",38939510,https://www.aclweb.org/anthology/2020.conll-1.48 +WS-1,WS-1.226,Cloze Distillation Improves Psychometric Predictive Power,Tiwalayo Eisape|Noga Zaslavsky|Roger Levy,"Contemporary autoregressive language models (LMs) trained purely on corpus data have been shown to capture numerous features of human incremental processing. However, past work has also suggested dissociations between corpus probabilities and human next-word predictions. Here we evaluate several state-of-the-art language models for their match to human next-word predictions and to reading time behavior from eye movements. We then propose a novel method for distilling the linguistic information implicit in human linguistic predictions into pre-trained LMs: Cloze Distillation. We apply this method to a baseline neural LM and show potential improvement in reading time prediction and generalization to held-out human cloze data.",38939511,https://www.aclweb.org/anthology/2020.conll-1.49 +WS-1,WS-1.234,Disentangling dialects: a neural approach to Indo-Aryan historical phonology and subgrouping,Chundra Cathcart|Taraka Rama,"This paper seeks to uncover patterns of sound change across Indo-Aryan languages using an LSTM encoder-decoder architecture. We augment our models with embeddings represent-ing language ID, part of speech, and other features such as word embeddings. We find that a highly augmented model shows highest accuracy in predicting held-out forms, and investigate other properties of interest learned by our models’ representations. We outline extensions to this architecture that can better capture variation in Indo-Aryan sound change.",38939512,https://www.aclweb.org/anthology/2020.conll-1.50 +WS-1,WS-1.247,"A Dataset for Linguistic Understanding, Visual Evaluation, and Recognition of Sign Languages: The K-RSL",Alfarabi Imashev|Medet Mukushev|Vadim Kimmelman|Anara Sandygulova,"The paper presents the first dataset that aims to serve interdisciplinary purposes for the utility of computer vision community and sign language linguistics. To date, a majority of Sign Language Recognition (SLR) approaches focus on recognising sign language as a manual gesture recognition problem. However, signers use other articulators: facial expressions, head and body position and movement to convey linguistic information. Given the important role of non-manual markers, this paper proposes a dataset and presents a use case to stress the importance of including non-manual features to improve the recognition accuracy of signs. To the best of our knowledge no prior publicly available dataset exists that explicitly focuses on non-manual components responsible for the grammar of sign languages. To this end, the proposed dataset contains 28250 videos of signs of high resolution and quality, with annotation of manual and non-manual components. We conducted a series of evaluations in order to investigate whether non-manual components would improve signs’ recognition accuracy. We release the dataset to encourage SLR researchers and help advance current progress in this area toward real-time sign language interpretation. Our dataset will be made publicly available at https://krslproject.github.io/krsl-corpus",38939513,https://www.aclweb.org/anthology/2020.conll-1.51 +WS-1,WS-1.258,From Dataset Recycling to Multi-Property Extraction and Beyond,Tomasz Dwojak|Michał Pietruszka|Łukasz Borchmann|Jakub Chłędowski|Filip Graliński,"This paper investigates various Transformer architectures on the WikiReading Information Extraction and Machine Reading Comprehension dataset. The proposed dual-source model outperforms the current state-of-the-art by a large margin. Next, we introduce WikiReading Recycled - a newly developed public dataset, and the task of multiple-property extraction. It uses the same data as WikiReading but does not inherit its predecessor’s identified disadvantages. In addition, we provide a human-annotated test set with diagnostic subsets for a detailed analysis of model performance.",38939514,https://www.aclweb.org/anthology/2020.conll-1.52 +WS-1,WS-1.259,How well does surprisal explain N400 amplitude under different experimental conditions?,James Michaelov|Benjamin Bergen,"We investigate the extent to which word surprisal can be used to predict a neural measure of human language processing difficulty—the N400. To do this, we use recurrent neural networks to calculate the surprisal of stimuli from previously published neurolinguistic studies of the N400. We find that surprisal can predict N400 amplitude in a wide range of cases, and the cases where it cannot do so provide valuable insight into the neurocognitive processes underlying the response.",38939515,https://www.aclweb.org/anthology/2020.conll-1.53 +WS-1,WS-1.26,Classifying Syntactic Errors in Learner Language,Leshem Choshen|Dmitry Nikolaev|Yevgeni Berzak|Omri Abend,"We present a method for classifying syntactic errors in learner language, namely errors whose correction alters the morphosyntactic structure of a sentence. The methodology builds on the established Universal Dependencies syntactic representation scheme, and provides complementary information to other error-classification systems. Unlike existing error classification methods, our method is applicable across languages, which we showcase by producing a detailed picture of syntactic errors in learner English and learner Russian. We further demonstrate the utility of the methodology for analyzing the outputs of leading Grammatical Error Correction (GEC) systems.",38939469,https://www.aclweb.org/anthology/2020.conll-1.7 +WS-1,WS-1.28,How to Probe Sentence Embeddings in Low-Resource Languages: On Structural Design Choices for Probing Task Evaluation,Steffen Eger|Johannes Daxenberger|Iryna Gurevych,"Sentence encoders map sentences to real valued vectors for use in downstream applications. To peek into these representations—e.g., to increase interpretability of their results—probing tasks have been designed which query them for linguistic knowledge. However, designing probing tasks for lesser-resourced languages is tricky, because these often lack largescale annotated data or (high-quality) dependency parsers as a prerequisite of probing task design in English. To investigate how to probe sentence embeddings in such cases, we investigate sensitivity of probing task results to structural design choices, conducting the first such large scale study. We show that design choices like size of the annotated probing dataset and type of classifier used for evaluation do (sometimes substantially) influence probing outcomes. We then probe embeddings in a multilingual setup with design choices that lie in a ‘stable region’, as we identify for English, and find that results on English do not transfer to other languages. Fairer and more comprehensive sentence-level probing evaluation should thus be carried out on multiple languages in the future.",38939470,https://www.aclweb.org/anthology/2020.conll-1.8 +WS-1,WS-1.29,Understanding the Source of Semantic Regularities in Word Embeddings,Hsiao-Yu Chiang|Jose Camacho-Collados|Zachary Pardos,"Semantic relations are core to how humans understand and express concepts in the real world using language. Recently, there has been a thread of research aimed at modeling these relations by learning vector representations from text corpora. Most of these approaches focus strictly on leveraging the co-occurrences of relationship word pairs within sentences. In this paper, we investigate the hypothesis that examples of a lexical relation in a corpus are fundamental to a neural word embedding’s ability to complete analogies involving the relation. Our experiments, in which we remove all known examples of a relation from training corpora, show only marginal degradation in analogy completion performance involving the removed relation. This finding enhances our understanding of neural word embeddings, showing that co-occurrence information of a particular semantic relation is the not the main source of their structural regularity.",38939471,https://www.aclweb.org/anthology/2020.conll-1.9 +WS-1,WS-1.38,Finding The Right One and Resolving it,Payal Khullar|Arghya Bhattacharya|Manish Shrivastava,"One-anaphora has figured prominently in theoretical linguistic literature, but computational linguistics research on the phenomenon is sparse. Not only that, the long standing linguistic controversy between the determinative and the nominal anaphoric element one has propagated in the limited body of computational work on one-anaphora resolution, making this task harder than it is. In the present paper, we resolve this by drawing from an adequate linguistic analysis of the word one in different syntactic environments - once again highlighting the significance of linguistic theory in Natural Language Processing (NLP) tasks. We prepare an annotated corpus marking actual instances of one-anaphora with their textual antecedents, and use the annotations to experiment with state-of-the art neural models for one-anaphora resolution. Apart from presenting a strong neural baseline for this task, we contribute a gold-standard corpus, which is, to the best of our knowledge, the biggest resource on one-anaphora till date.",38939472,https://www.aclweb.org/anthology/2020.conll-1.10 +WS-1,WS-1.49,Bridging Information-Seeking Human Gaze and Machine Reading Comprehension,Jonathan Malmaud|Roger Levy|Yevgeni Berzak,"In this work, we analyze how human gaze during reading comprehension is conditioned on the given reading comprehension question, and whether this signal can be beneficial for machine reading comprehension. To this end, we collect a new eye-tracking dataset with a large number of participants engaging in a multiple choice reading comprehension task. Our analysis of this data reveals increased fixation times over parts of the text that are most relevant for answering the question. Motivated by this finding, we propose making automated reading comprehension more human-like by mimicking human information-seeking reading behavior during reading comprehension. We demonstrate that this approach leads to performance gains on multiple choice question answering in English for a state-of-the-art reading comprehension model.",38939473,https://www.aclweb.org/anthology/2020.conll-1.11 +WS-1,WS-1.510_F,Dynamic Data Selection for Curriculum Learning via Ability Estimation,John P. Lalor|Hong Yu,"Curriculum learning methods typically rely on heuristics to estimate the difficulty of training examples or the ability of the model. In this work, we propose replacing difficulty heuristics with learned difficulty parameters. We also propose Dynamic Data selection for Curriculum Learning via Ability Estimation (DDaCLAE), a strategy that probes model ability at each training epoch to select the best training examples at that point. We show that models using learned difficulty and/or ability outperform heuristic-based curriculum learning models on the GLUE classification tasks.",38940641,https://www.aclweb.org/anthology/2020.findings-emnlp.48 +WS-1,WS-1.561_F,From Language to Language-ish: How Brain-Like is an LSTM's Representation of Nonsensical Language Stimuli?,Maryam Hashemzadeh|Greta Kaufeld|Martha White|Andrea E. Martin|Alona Fyshe,"The representations generated by many models of language (word embeddings, recurrent neural networks and transformers) correlate to brain activity recorded while people read. However, these decoding results are usually based on the brain’s reaction to syntactically and semantically sound language stimuli. In this study, we asked: how does an LSTM (long short term memory) language model, trained (by and large) on semantically and syntactically intact language, represent a language sample with degraded semantic or syntactic information? Does the LSTM representation still resemble the brain’s reaction? We found that, even for some kinds of nonsensical language, there is a statistically significant relationship between the brain’s activity and the representations of an LSTM. This indicates that, at least in some instances, LSTMs and the human brain handle nonsensical data similarly.",38940642,https://www.aclweb.org/anthology/2020.findings-emnlp.57 +WS-1,WS-1.59,A Corpus of Very Short Scientific Summaries,Yifan Chen|Tamara Polajnar|Colin Batchelor|Simone Teufel,"We present a new summarisation task, taking scientific articles and producing journal table-of-contents entries in the chemistry domain. These are one- or two-sentence author-written summaries that present the key findings of a paper. This is a first look at this summarisation task with an open access publication corpus consisting of titles and abstracts, as input texts, and short author-written advertising blurbs, as the ground truth. We introduce the dataset and evaluate it with state-of-the-art summarisation methods.",38939474,https://www.aclweb.org/anthology/2020.conll-1.12 +WS-1,WS-1.61,Recurrent babbling: evaluating the acquisition of grammar from limited input data,Ludovica Pannitto|Aurélie Herbelot,"Recurrent Neural Networks (RNNs) have been shown to capture various aspects of syntax from raw linguistic input. In most previous experiments, however, learning happens over unrealistic corpora, which do not reflect the type and amount of data a child would be exposed to. This paper remedies this state of affairs by training an LSTM over a realistically sized subset of child-directed input. The behaviour of the network is analysed over time using a novel methodology which consists in quantifying the level of grammatical abstraction in the model’s generated output (its ‘babbling’), compared to the language it has been exposed to. We show that the LSTM indeed abstracts new structures as learning proceeds.",38939475,https://www.aclweb.org/anthology/2020.conll-1.13 +WS-1,WS-1.63,Explaining the efficiency of communication: How communicators can reduce their computational burden through interaction,Jacqueline Van Arkel|Marieke Woensdregt|Mark Dingemanse|Mark Blokpoel,"How can people communicate successfully while keeping resource costs low in the face of ambiguity? We present a principled theoretical analysis comparing two strategies for disambiguation in communication: (i) pragmatic reasoning, where communicators reason about each other, and (ii) other-initiated repair, where communicators signal and resolve trouble interactively. Using agent-based simulations and computational complexity analyses, we compare the efficiency of these strategies in terms of communicative success, computation cost and interaction cost. We show that agents with a simple repair mechanism can increase efficiency, compared to pragmatic agents, by reducing their computational burden at the cost of longer interactions. We also find that efficiency is highly contingent on the mechanism, highlighting the importance of explicit formalisation and computational rigour.",38939476,https://www.aclweb.org/anthology/2020.conll-1.14 +WS-1,WS-1.69,Acquiring language from speech by learning to remember and predict,Cory Shain|Micha Elsner,"Classical accounts of child language learning invoke memory limits as a pressure to discover sparse, language-like representations of speech, while more recent proposals stress the importance of prediction for language learning. In this study, we propose a broad-coverage unsupervised neural network model to test memory and prediction as sources of signal by which children might acquire language directly from the perceptual stream. Our model embodies several likely properties of real-time human cognition: it is strictly incremental, it encodes speech into hierarchically organized labeled segments, it allows interactive top-down and bottom-up information flow, it attempts to model its own sequence of latent representations, and its objective function only recruits local signals that are plausibly supported by human working memory capacity. We show that much phonemic structure is learnable from unlabeled speech on the basis of these local signals. We further show that remembering the past and predicting the future both contribute to the linguistic content of acquired representations, and that these contributions are at least partially complementary.",38939477,https://www.aclweb.org/anthology/2020.conll-1.15 +WS-1,WS-1.69_F,"Stay Hungry, Stay Focused: Generating Informative and Specific Questions in Information-Seeking Conversations",Peng Qi|Yuhao Zhang|Christopher D. Manning,"We investigate the problem of generating informative questions in information-asymmetric conversations. Unlike previous work on question generation which largely assumes knowledge of what the answer might be, we are interested in the scenario where the questioner is not given the context from which answers are drawn, but must reason pragmatically about how to acquire new information, given the shared conversation history. We identify two core challenges: (1) formally defining the informativeness of potential questions, and (2) exploring the prohibitively large space of potential questions to find the good candidates. To generate pragmatic questions, we use reinforcement learning to optimize an informativeness metric we propose, combined with a reward function designed to promote more specific questions. We demonstrate that the resulting pragmatic questioner substantially improves the informativeness and specificity of questions generated over a baseline model, as evaluated by our metrics as well as humans.",38940640,https://www.aclweb.org/anthology/2020.findings-emnlp.3 +WS-1,WS-1.7,Enriching Word Embeddings with Temporal and Spatial Information,Hongyu Gong|Suma Bhat|Pramod Viswanath,"The meaning of a word is closely linked to sociocultural factors that can change over time and location, resulting in corresponding meaning changes. Taking a global view of words and their meanings in a widely used language, such as English, may require us to capture more refined semantics for use in time-specific or location-aware situations, such as the study of cultural trends or language use. However, popular vector representations for words do not adequately include temporal or spatial information. In this work, we present a model for learning word representation conditioned on time and location. In addition to capturing meaning changes over time and location, we require that the resulting word embeddings retain salient semantic and geometric properties. We train our model on time- and location-stamped corpora, and show using both quantitative and qualitative evaluations that it can capture semantics across time and locations. We note that our model compares favorably with the state-of-the-art for time-specific embedding, and serves as a new benchmark for location-specific embeddings.",38939463,https://www.aclweb.org/anthology/2020.conll-1.1 +WS-1,WS-1.70,Identifying Incorrect Labels in the CoNLL-2003 Corpus,Frederick Reiss|Hong Xu|Bryan Cutler|Karthik Muthuraman|Zachary Eichenberger,"The CoNLL-2003 corpus for English-language named entity recognition (NER) is one of the most influential corpora for NER model research. A large number of publications, including many landmark works, have used this corpus as a source of ground truth for NER tasks. In this paper, we examine this corpus and identify over 1300 incorrect labels (out of 35089 in the corpus). In particular, the number of incorrect labels in the test fold is comparable to the number of errors that state-of-the-art models make when running inference over this corpus. We describe the process by which we identified these incorrect labels, using novel variants of techniques from semi-supervised learning. We also summarize the types of errors that we found, and we revisit several recent results in NER in light of the corrected data. Finally, we show experimentally that our corrections to the corpus have a positive impact on three state-of-the-art models.",38939478,https://www.aclweb.org/anthology/2020.conll-1.16 +WS-1,WS-1.73,When is a bishop not like a rook? When it's like a rabbi! Multi-prototype BERT embeddings for estimating semantic relationships,Gabriella Chronis|Katrin Erk,"This paper investigates contextual language models, which produce token representations, as a resource for lexical semantics at the word or type level. We construct multi-prototype word embeddings from bert-base-uncased (Devlin et al., 2018). These embeddings retain contextual knowledge that is critical for some type-level tasks, while being less cumbersome and less subject to outlier effects than exemplar models. Similarity and relatedness estimation, both type-level tasks, benefit from this contextual knowledge, indicating the context-sensitivity of these processes. BERT’s token level knowledge also allows the testing of a type-level hypothesis about lexical abstractness, demonstrating the relationship between token-level phenomena and type-level concreteness ratings. Our findings provide important insight into the interpretability of BERT: layer 7 approximates semantic similarity, while the final layer (11) approximates relatedness.",38939479,https://www.aclweb.org/anthology/2020.conll-1.17 +WS-1,WS-1.8,Interpreting Attention Models with Human Visual Attention in Machine Reading Comprehension,Ekta Sood|Simon Tannert|Diego Frassinelli|Andreas Bulling|Ngoc Thang Vu,"While neural networks with attention mechanisms have achieved superior performance on many natural language processing tasks, it remains unclear to which extent learned attention resembles human visual attention. In this paper, we propose a new method that leverages eye-tracking data to investigate the relationship between human visual attention and neural attention in machine reading comprehension. To this end, we introduce a novel 23 participant eye tracking dataset - MQA-RC, in which participants read movie plots and answered pre-defined questions. We compare state of the art networks based on long short-term memory (LSTM), convolutional neural models (CNN) and XLNet Transformer architectures. We find that higher similarity to human attention and performance significantly correlates to the LSTM and CNN models. However, we show this relationship does not hold true for the XLNet models – despite the fact that the XLNet performs best on this challenging task. Our results suggest that different architectures seem to learn rather different neural attention strategies and similarity of neural to human attention does not guarantee best performance.",38939464,https://www.aclweb.org/anthology/2020.conll-1.2 +WS-1,WS-1.83,Processing effort is a poor predictor of cross-linguistic word order frequency,Brennan Gonering|Emily Morgan,"Some have argued that word orders which are more difficult to process should be rarer cross-linguistically. Our current study fails to replicate the results of Maurits, Navarro, and Perfors (2010), who used an entropy-based Uniform Information Density (UID) measure to moderately predict the Greenbergian typology of transitive word orders. We additionally report an inability of three measures of processing difficulty — entropy-based UID, surprisal-based UID, and pointwise mutual information — to correctly predict the correct typological distribution, using transitive constructions from 20 languages in the Universal Dependencies project (version 2.5). However, our conclusions are limited by data sparsity.",38939480,https://www.aclweb.org/anthology/2020.conll-1.18 +WS-1,WS-1.88,Relations between comprehensibility and adequacy errors in machine translation output,Maja Popović,"This work presents a detailed analysis of translation errors perceived by readers as comprehensibility and/or adequacy issues. The main finding is that good comprehensibility, similarly to good fluency, can mask a number of adequacy errors. Of all major adequacy errors, 30% were fully comprehensible, thus fully misleading the reader to accept the incorrect information. Another 25% of major adequacy errors were perceived as almost comprehensible, thus being potentially misleading. Also, a vast majority of omissions (about 70%) is hidden by comprehensibility. Further analysis of misleading translations revealed that the most frequent error types are ambiguity, mistranslation, noun phrase error, word-by-word translation, untranslated word, subject-verb agreement, and spelling error in the source text. However, none of these error types appears exclusively in misleading translations, but are also frequent in fully incorrect (incomprehensible inadequate) and discarded correct (incomprehensible adequate) translations. Deeper analysis is needed to potentially detect underlying phenomena specifically related to misleading translations.",38939481,https://www.aclweb.org/anthology/2020.conll-1.19 +WS-1,WS-1.96,Cross-lingual Embeddings Reveal Universal and Lineage-Specific Patterns in Grammatical Gender Assignment,Hartger Veeman|Marc Allassonnière-Tang|Aleksandrs Berdicevskis|Ali Basirat,"Grammatical gender is assigned to nouns differently in different languages. Are all factors that influence gender assignment idiosyncratic to languages or are there any that are universal? Using cross-lingual aligned word embeddings, we perform two experiments to address these questions about language typology and human cognition. In both experiments, we predict the gender of nouns in language X using a classifier trained on the nouns of language Y, and take the classifier’s accuracy as a measure of transferability of gender systems. First, we show that for 22 Indo-European languages the transferability decreases as the phylogenetic distance increases. This correlation supports the claim that some gender assignment factors are idiosyncratic, and as the languages diverge, the proportion of shared inherited idiosyncrasies diminishes. Second, we show that when the classifier is trained on two Afro-Asiatic languages and tested on the same 22 Indo-European languages (or vice versa), its performance is still significantly above the chance baseline, thus showing that universal factors exist and, moreover, can be captured by word embeddings. When the classifier is tested across families and on inanimate nouns only, the performance is still above baseline, indicating that the universal factors are not limited to biological sex.",38939482,https://www.aclweb.org/anthology/2020.conll-1.20 +WS-1,WS-1.Shared1,Hitachi at MRP 2020: Text-to-Graph-Notation Transducer,Hiroaki Ozaki|Gaku Morio|Yuta Koreeda|Terufumi Morishita|Toshinori Miyoshi,"This paper presents our proposed parser for the shared task on Meaning Representation Parsing (MRP 2020) at CoNLL, where participant systems were required to parse five types of graphs in different languages. We propose to unify these tasks as a text-to-graph-notation transduction in which we convert an input text into a graph notation. To this end, we designed a novel Plain Graph Notation (PGN) that handles various graphs universally. Then, our parser predicts a PGN-based sequence by leveraging Transformers and biaffine attentions. Notably, our parser can handle any PGN-formatted graphs with fewer framework-specific modifications. As a result, ensemble versions of the parser tied for 1st place in both cross-framework and cross-lingual tracks.",38941228,https://www.aclweb.org/anthology/2020.conll-shared.4 +WS-1,WS-1.Shared2,HIT-SCIR at MRP 2020: Transition-based Parser and Iterative Inference Parser,Longxu Dou|Yunlong Feng|Yuqiu Ji|Wanxiang Che|Ting Liu,"This paper describes our submission system (HIT-SCIR) for the CoNLL 2020 shared task: Cross-Framework and Cross-Lingual Meaning Representation Parsing. The task includes five frameworks for graph-based meaning representations, i.e., UCCA, EDS, PTG, AMR, and DRG. Our solution consists of two sub-systems: transition-based parser for Flavor (1) frameworks (UCCA, EDS, PTG) and iterative inference parser for Flavor (2) frameworks (DRG, AMR). In the final evaluation, our system is ranked 3rd among the seven team both in Cross-Framework Track and Cross-Lingual Track, with the macro-averaged MRP F1 score of 0.81/0.69.",38941229,https://www.aclweb.org/anthology/2020.conll-shared.6 +WS-1,WS-1.Shared3,HUJI-KU at MRP 2020: Two Transition-based Neural Parsers,Ofir Arviv|Ruixiang Cui|Daniel Hershcovich,"This paper describes the HUJI-KU system submission to the shared task on CrossFramework Meaning Representation Parsing (MRP) at the 2020 Conference for Computational Language Learning (CoNLL), employing TUPA and the HIT-SCIR parser, which were, respectively, the baseline system and winning system in the 2019 MRP shared task. Both are transition-based parsers using BERT contextualized embeddings. We generalized TUPA to support the newly-added MRP frameworks and languages, and experimented with multitask learning with the HIT-SCIR parser. We reached 4th place in both the crossframework and cross-lingual tracks.",38941230,https://www.aclweb.org/anthology/2020.conll-shared.7 +WS-1,WS-1.Shared4,JBNU at MRP 2020: AMR Parsing Using a Joint State Model for Graph-Sequence Iterative Inference,Seung-Hoon Na|Jinwoo Min,"This paper describes the Jeonbuk National University (JBNU) system for the 2020 shared task on Cross-Framework Meaning Representation Parsing at the Conference on Computational Natural Language Learning. Among the five frameworks, we address only the abstract meaning representation framework and propose a joint state model for the graph-sequence iterative inference of (Cai and Lam, 2020) for a simplified graph-sequence inference. In our joint state model, we update only a single joint state vector during the graph-sequence inference process instead of keeping the dual state vectors, and all other components are exactly the same as in (Cai and Lam, 2020).",38941231,https://www.aclweb.org/anthology/2020.conll-shared.8 +WS-1,WS-1.Shared5,ÚFAL at MRP 2020: Permutation-invariant Semantic Parsing in PERIN,David Samuel|Milan Straka,"We present PERIN, a novel permutation-invariant approach to sentence-to-graph semantic parsing. PERIN is a versatile, cross-framework and language independent architecture for universal modeling of semantic structures. Our system participated in the CoNLL 2020 shared task, Cross-Framework Meaning Representation Parsing (MRP 2020), where it was evaluated on five different frameworks (AMR, DRG, EDS, PTG and UCCA) across four languages. PERIN was one of the winners of the shared task. The source code and pretrained models are available at http://www.github.com/ufal/perin.",38941232,https://www.aclweb.org/anthology/2020.conll-shared.5 +WS-1,WS-1.Shared6,FGD at MRP 2020: Prague Tectogrammatical Graphs,Daniel Zeman|Jan Hajic,Prague Tectogrammatical Graphs (PTG) is a meaning representation framework that originates in the tectogrammatical layer of the Prague Dependency Treebank (PDT) and is theoretically founded in Functional Generative Description of language (FGD). PTG in its present form has been prepared for the CoNLL 2020 shared task on Cross-Framework Meaning Representation Parsing (MRP). It is generated automatically from the Prague treebanks and stored in the JSON-based MRP graph interchange format. The conversion is partially lossy; in this paper we describe what part of annotation was included and how it is represented in PTG.,38941233,https://www.aclweb.org/anthology/2020.conll-shared.3 +WS-1,WS-1.Shared7,DRS at MRP 2020: Dressing up Discourse Representation Structures as Graphs,Lasha Abzianidze|Johan Bos|Stephan Oepen,"Discourse Representation Theory (DRT) is a formal account for representing the meaning of natural language discourse. Meaning in DRT is modeled via a Discourse Representation Structure (DRS), a meaning representation with a model-theoretic interpretation, which is usually depicted as nested boxes. In contrast, a directed labeled graph is a common data structure used to encode semantics of natural language texts. The paper describes the procedure of dressing up DRSs as directed labeled graphs to include DRT as a new framework in the 2020 shared task on Cross-Framework and Cross-Lingual Meaning Representation Parsing. Since one of the goals of the shared task is to encourage unified models for several semantic graph frameworks, the conversion procedure was biased towards making the DRT graph framework somewhat similar to other graph-based meaning representation frameworks.",38941234,https://www.aclweb.org/anthology/2020.conll-shared.2 WS-1,WS-1.Shared8,MRP 2020: The Second Shared Task on Cross-Framework and Cross-Linguistic Meaning Representation Parsing,Stephan Oepen|Omri Abend|Lasha Abzianidze|Johan Bos|Jan Hajic|Daniel Hershcovich|Bin Li|Tim O'Gorman|Nianwen Xue|Daniel Zeman,,38941235, WS-10,WS-10.11,Geocoding with multi-level loss for spatial language representation,Sayali Kulkarni|Shailee Jain|Mohammad Javad Hosseini|Jason Baldridge|Eugene Ie|Li Zhang,,38940083, WS-10,WS-10.12,SpaRTQA: A Textual Question Answering Benchmark for Spatial Reasoning,Roshanak Mirzaee|Hossein Rajaby Faghihi|Parisa Kordjamshidi,,38940084, WS-10,WS-10.13,Vision-and-Language Navigation by Reasoning over Spatial Configurations,Yue Zhang|Quan Guo|Parisa Kordjamshidi,,38940085, -WS-10,WS-10.1453,RMM: A Recursive Mental Model for Dialogue Navigation,Homero Roman Roman|Yonatan Bisk|Jesse Thomason|Asli Celikyilmaz|Jianfeng Gao,,38940095,https://www.aclweb.org/anthology/2020.findings-emnlp.157 -WS-10,WS-10.1595,Robust and Interpretable Grounding of Spatial References with Relation Networks,Tsung-Yen Yang|Andrew S. Lan|Karthik Narasimhan,,38940094,https://www.aclweb.org/anthology/2020.findings-emnlp.172 -WS-10,WS-10.2,They are not all alike: answering different spatial questions requires different grounding strategies,Alberto Testoni|Claudio Greco|Tobias Bianchi|Mauricio Mazuecos|Agata Marcante|Luciana Benotti|Raffaella Bernardi,,38940076,https://www.aclweb.org/anthology/2020.splu-1.4 -WS-10,WS-10.2904,ARRAMON: A Joint Navigation-Assembly Instruction Interpretation Task in Dynamic Environments,Hyounghun Kim|Abhay Zala|Graham Burri|Hao Tan|Mohit Bansal,,38940093,https://www.aclweb.org/anthology/2020.findings-emnlp.348 -WS-10,WS-10.3,"Categorisation, Typicality Object-Specific Features in Spatial Referring Expressions",Adam Richard-Bollans|Anthony Cohn|Lucía Gómez Álvarez,,38940077,https://www.aclweb.org/anthology/2020.splu-1.5 -WS-10,WS-10.3302,Visually-Grounded Planning without Vision: Language Models Infer Detailed Plans from High-level Instructions,Peter A. Jansen,,38940098,https://www.aclweb.org/anthology/2020.findings-emnlp.395 -WS-10,WS-10.3382,Decoding Language Spatial Relations to 2D Spatial Arrangements,Gorjan Radevski|Guillem Collell|Marie-Francine Moens|Tinne Tuytelaars,,38940092,https://www.aclweb.org/anthology/2020.findings-emnlp.408 -WS-10,WS-10.3466,Language-Conditioned Feature Pyramids for Visual Selection Tasks,Taichi Iki|Akiko Aizawa,,38940091,https://www.aclweb.org/anthology/2020.findings-emnlp.420 -WS-10,WS-10.5,BERT-based Spatial Information Extraction,Hyeong Jin Shin|Jeong Yeon Park|Dae Bum Yuk|Jae Sung Lee,,38940078,https://www.aclweb.org/anthology/2020.splu-1.2 -WS-10,WS-10.6,A Cognitively Motivated Approach to Spatial Information Extraction,Chao Xu|Emmanuelle-Anna Dietz Saldanha|Dagmar Gromann|Beihai Zhou,,38940079,https://www.aclweb.org/anthology/2020.splu-1.3 -WS-10,WS-10.676,A Linguistic Analysis of Visually Grounded Dialogues Based on Spatial Expressions,Takuma Udagawa|Takato Yamazaki|Akiko Aizawa,,38940097,https://www.aclweb.org/anthology/2020.findings-emnlp.67 -WS-10,WS-10.7,A Hybrid Deep Learning Approach for Spatial Trigger Extraction from Radiology Reports,Surabhi Datta|Kirk Roberts,,38940080,https://www.aclweb.org/anthology/2020.splu-1.6 -WS-10,WS-10.8,An Element-wise Visual-enhanced BiLSTM-CRF Model for Location Name Recognition,Takuya Komada|Takashi Inui,,38940081,https://www.aclweb.org/anthology/2020.splu-1.1 -WS-10,WS-10.857,LiMiT: The Literal Motion in Text Dataset,Irene Manotas|Ngoc Phuoc An Vo|Vadim Sheinin,,38940096,https://www.aclweb.org/anthology/2020.findings-emnlp.88 -WS-10,WS-10.9,Retouchdown: Releasing Touchdown on StreetLearn as a Public Resource for Language Grounding Tasks in Street View,Harsh Mehta|Yoav Artzi|Jason Baldridge|Eugene Ie|Piotr Mirowski,,38940082,https://www.aclweb.org/anthology/2020.splu-1.7 +WS-10,WS-10.1453,RMM: A Recursive Mental Model for Dialogue Navigation,Homero Roman Roman|Yonatan Bisk|Jesse Thomason|Asli Celikyilmaz|Jianfeng Gao,"Language-guided robots must be able to both ask humans questions and understand answers. Much existing work focuses only on the latter. In this paper, we go beyond instruction following and introduce a two-agent task where one agent navigates and asks questions that a second, guiding agent answers. Inspired by theory of mind, we propose the Recursive Mental Model (RMM). The navigating agent models the guiding agent to simulate answers given candidate generated questions. The guiding agent in turn models the navigating agent to simulate navigation steps it would take to generate answers. We use the progress agents make towards the goal as a reinforcement learning reward signal to directly inform not only navigation actions, but also both question and answer generation. We demonstrate that RMM enables better generalization to novel environments. Interlocutor modelling may be a way forward for human-agent RMM where robots need to both ask and answer questions.",38940095,https://www.aclweb.org/anthology/2020.findings-emnlp.157 +WS-10,WS-10.1595,Robust and Interpretable Grounding of Spatial References with Relation Networks,Tsung-Yen Yang|Andrew S. Lan|Karthik Narasimhan,"Learning representations of spatial references in natural language is a key challenge in tasks like autonomous navigation and robotic manipulation. Recent work has investigated various neural architectures for learning multi-modal representations for spatial concepts. However, the lack of explicit reasoning over entities makes such approaches vulnerable to noise in input text or state observations. In this paper, we develop effective models for understanding spatial references in text that are robust and interpretable, without sacrificing performance. We design a text-conditioned relation network whose parameters are dynamically computed with a cross-modal attention module to capture fine-grained spatial relations between entities. This design choice provides interpretability of learned intermediate outputs. Experiments across three tasks demonstrate that our model achieves superior performance, with a 17% improvement in predicting goal locations and a 15% improvement in robustness compared to state-of-the-art systems.",38940094,https://www.aclweb.org/anthology/2020.findings-emnlp.172 +WS-10,WS-10.2,They are not all alike: answering different spatial questions requires different grounding strategies,Alberto Testoni|Claudio Greco|Tobias Bianchi|Mauricio Mazuecos|Agata Marcante|Luciana Benotti|Raffaella Bernardi,"In this paper, we study the grounding skills required to answer spatial questions asked by humans while playing the GuessWhat?! game. We propose a classification for spatial questions dividing them into absolute, relational, and group questions. We build a new answerer model based on the LXMERT multimodal transformer and we compare a baseline with and without visual features of the scene. We are interested in studying how the attention mechanisms of LXMERT are used to answer spatial questions since they require putting attention on more than one region simultaneously and spotting the relation holding among them. We show that our proposed model outperforms the baseline by a large extent (9.70% on spatial questions and 6.27% overall). By analyzing LXMERT errors and its attention mechanisms, we find that our classification helps to gain a better understanding of the skills required to answer different spatial questions.",38940076,https://www.aclweb.org/anthology/2020.splu-1.4 +WS-10,WS-10.2904,ARRAMON: A Joint Navigation-Assembly Instruction Interpretation Task in Dynamic Environments,Hyounghun Kim|Abhay Zala|Graham Burri|Hao Tan|Mohit Bansal,"For embodied agents, navigation is an important ability but not an isolated goal. Agents are also expected to perform specific tasks after reaching the target location, such as picking up objects and assembling them into a particular arrangement. We combine Vision-andLanguage Navigation, assembling of collected objects, and object referring expression comprehension, to create a novel joint navigation-and-assembly task, named ARRAMON. During this task, the agent (similar to a PokeMON GO player) is asked to find and collect different target objects one-by-one by navigating based on natural language (English) instructions in a complex, realistic outdoor environment, but then also ARRAnge the collected objects part-by-part in an egocentric grid-layout environment. To support this task, we implement a 3D dynamic environment simulator and collect a dataset with human-written navigation and assembling instructions, and the corresponding ground truth trajectories. We also filter the collected instructions via a verification stage, leading to a total of 7.7K task instances (30.8K instructions and paths). We present results for several baseline models (integrated and biased) and metrics (nDTW, CTC, rPOD, and PTC), and the large model-human performance gap demonstrates that our task is challenging and presents a wide scope for future work.",38940093,https://www.aclweb.org/anthology/2020.findings-emnlp.348 +WS-10,WS-10.3,"Categorisation, Typicality Object-Specific Features in Spatial Referring Expressions",Adam Richard-Bollans|Anthony Cohn|Lucía Gómez Álvarez,"Various accounts of cognition and semantic representations have highlighted that, for some concepts, different factors may influence category and typicality judgements. In particular, some features may be more salient in categorisation tasks while other features are more salient when assessing typicality. In this paper we explore the extent to which this is the case for English spatial prepositions and discuss the implications for pragmatic strategies and semantic models. We hypothesise that object-specific features — related to object properties and affordances — are more salient in categorisation, while geometric and physical relationships between objects are more salient in typicality judgements. In order to test this hypothesis we conducted a study using virtual environments to collect both category and typicality judgements in 3D scenes. Based on the collected data we cannot verify the hypothesis and conclude that object-specific features appear to be salient in both category and typicality judgements, further evidencing the need to include these types of features in semantic models.",38940077,https://www.aclweb.org/anthology/2020.splu-1.5 +WS-10,WS-10.3302,Visually-Grounded Planning without Vision: Language Models Infer Detailed Plans from High-level Instructions,Peter A. Jansen,"The recently proposed ALFRED challenge task aims for a virtual robotic agent to complete complex multi-step everyday tasks in a virtual home environment from high-level natural language directives, such as “put a hot piece of bread on a plate”. Currently, the best-performing models are able to complete less than 1% of these tasks successfully. In this work we focus on modeling the translation problem of converting natural language directives into detailed multi-step sequences of actions that accomplish those goals in the virtual environment. We empirically demonstrate that it is possible to generate gold multi-step plans from language directives alone without any visual input in 26% of unseen cases. When a small amount of visual information, the starting location in the virtual environment, is incorporated, our best-performing GPT-2 model successfully generates gold command sequences in 58% of cases, suggesting contextualized language models may provide strong planning modules for grounded virtual agents.",38940098,https://www.aclweb.org/anthology/2020.findings-emnlp.395 +WS-10,WS-10.3382,Decoding Language Spatial Relations to 2D Spatial Arrangements,Gorjan Radevski|Guillem Collell|Marie-Francine Moens|Tinne Tuytelaars,"We address the problem of multimodal spatial understanding by decoding a set of language-expressed spatial relations to a set of 2D spatial arrangements in a multi-object and multi-relationship setting. We frame the task as arranging a scene of clip-arts given a textual description. We propose a simple and effective model architecture Spatial-Reasoning Bert (SR-Bert), trained to decode text to 2D spatial arrangements in a non-autoregressive manner. SR-Bert can decode both explicit and implicit language to 2D spatial arrangements, generalizes to out-of-sample data to a reasonable extent and can generate complete abstract scenes if paired with a clip-arts predictor. Finally, we qualitatively evaluate our method with a user study, validating that our generated spatial arrangements align with human expectation.",38940092,https://www.aclweb.org/anthology/2020.findings-emnlp.408 +WS-10,WS-10.3466,Language-Conditioned Feature Pyramids for Visual Selection Tasks,Taichi Iki|Akiko Aizawa,"Referring expression comprehension, which is the ability to locate language to an object in an image, plays an important role in creating common ground. Many models that fuse visual and linguistic features have been proposed. However, few models consider the fusion of linguistic features with multiple visual features with different sizes of receptive fields, though the proper size of the receptive field of visual features intuitively varies depending on expressions. In this paper, we introduce a neural network architecture that modulates visual features with varying sizes of receptive field by linguistic features. We evaluate our architecture on tasks related to referring expression comprehension in two visual dialogue games. The results show the advantages and broad applicability of our architecture. Source code is available at https://github.com/Alab-NII/lcfp .",38940091,https://www.aclweb.org/anthology/2020.findings-emnlp.420 +WS-10,WS-10.5,BERT-based Spatial Information Extraction,Hyeong Jin Shin|Jeong Yeon Park|Dae Bum Yuk|Jae Sung Lee,"Spatial information extraction is essential to understand geographical information in text. This task is largely divided to two subtasks: spatial element extraction and spatial relation extraction. In this paper, we utilize BERT (Devlin et al., 2018), which is very effective for many natural language processing applications. We propose a BERT-based spatial information extraction model, which uses BERT for spatial element extraction and R-BERT (Wu and He, 2019) for spatial relation extraction. The model was evaluated with the SemEval 2015 dataset. The result showed a 15.4% point increase in spatial element extraction and an 8.2% point increase in spatial relation extraction in comparison to the baseline model (Nichols and Botros, 2015).",38940078,https://www.aclweb.org/anthology/2020.splu-1.2 +WS-10,WS-10.6,A Cognitively Motivated Approach to Spatial Information Extraction,Chao Xu|Emmanuelle-Anna Dietz Saldanha|Dagmar Gromann|Beihai Zhou,"Automatic extraction of spatial information from natural language can boost human-centered applications that rely on spatial dynamics. The field of cognitive linguistics has provided theories and cognitive models to address this task. Yet, existing solutions tend to focus on specific word classes, subject areas, or machine learning techniques that cannot provide cognitively plausible explanations for their decisions. We propose an automated spatial semantic analysis (ASSA) framework building on grammar and cognitive linguistic theories to identify spatial entities and relations, bringing together methods of spatial information extraction and cognitive frameworks on spatial language. The proposed rule-based and explainable approach contributes constructions and preposition schemas and outperforms previous solutions on the CLEF-2017 standard dataset.",38940079,https://www.aclweb.org/anthology/2020.splu-1.3 +WS-10,WS-10.676,A Linguistic Analysis of Visually Grounded Dialogues Based on Spatial Expressions,Takuma Udagawa|Takato Yamazaki|Akiko Aizawa,"Recent models achieve promising results in visually grounded dialogues. However, existing datasets often contain undesirable biases and lack sophisticated linguistic analyses, which make it difficult to understand how well current models recognize their precise linguistic structures. To address this problem, we make two design choices: first, we focus on OneCommon Corpus (CITATION), a simple yet challenging common grounding dataset which contains minimal bias by design. Second, we analyze their linguistic structures based on spatial expressions and provide comprehensive and reliable annotation for 600 dialogues. We show that our annotation captures important linguistic structures including predicate-argument structure, modification and ellipsis. In our experiments, we assess the model’s understanding of these structures through reference resolution. We demonstrate that our annotation can reveal both the strengths and weaknesses of baseline models in essential levels of detail. Overall, we propose a novel framework and resource for investigating fine-grained language understanding in visually grounded dialogues.",38940097,https://www.aclweb.org/anthology/2020.findings-emnlp.67 +WS-10,WS-10.7,A Hybrid Deep Learning Approach for Spatial Trigger Extraction from Radiology Reports,Surabhi Datta|Kirk Roberts,"Radiology reports contain important clinical information about patients which are often tied through spatial expressions. Spatial expressions (or triggers) are mainly used to describe the positioning of radiographic findings or medical devices with respect to some anatomical structures. As the expressions result from the mental visualization of the radiologist’s interpretations, they are varied and complex. The focus of this work is to automatically identify the spatial expression terms from three different radiology sub-domains. We propose a hybrid deep learning-based NLP method that includes – 1) generating a set of candidate spatial triggers by exact match with the known trigger terms from the training data, 2) applying domain-specific constraints to filter the candidate triggers, and 3) utilizing a BERT-based classifier to predict whether a candidate trigger is a true spatial trigger or not. The results are promising, with an improvement of 24 points in the average F1 measure compared to a standard BERT-based sequence labeler.",38940080,https://www.aclweb.org/anthology/2020.splu-1.6 +WS-10,WS-10.8,An Element-wise Visual-enhanced BiLSTM-CRF Model for Location Name Recognition,Takuya Komada|Takashi Inui,"In recent years, previous studies have used visual information in named entity recognition (NER) for social media posts with attached images. However, these methods can only be applied to documents with attached images. In this paper, we propose a NER method that can use element-wise visual information for any documents by using image data corresponding to each word in the document. The proposed method obtains element-wise image data using an image retrieval engine, to be used as extra features in the neural NER model. Experimental results on the standard Japanese NER dataset show that the proposed method achieves a higher F1 value (89.67%) than a baseline method, demonstrating the effectiveness of using element-wise visual information.",38940081,https://www.aclweb.org/anthology/2020.splu-1.1 +WS-10,WS-10.857,LiMiT: The Literal Motion in Text Dataset,Irene Manotas|Ngoc Phuoc An Vo|Vadim Sheinin,"Motion recognition is one of the basic cognitive capabilities of many life forms, yet identifying motion of physical entities in natural language have not been explored extensively and empirically. We present the Literal-Motion-in-Text (LiMiT) dataset, a large human-annotated collection of English text sentences describing physical occurrence of motion, with annotated physical entities in motion. We describe the annotation process for the dataset, analyze its scale and diversity, and report results of several baseline models. We also present future research directions and applications of the LiMiT dataset and share it publicly as a new resource for the research community.",38940096,https://www.aclweb.org/anthology/2020.findings-emnlp.88 +WS-10,WS-10.9,Retouchdown: Releasing Touchdown on StreetLearn as a Public Resource for Language Grounding Tasks in Street View,Harsh Mehta|Yoav Artzi|Jason Baldridge|Eugene Ie|Piotr Mirowski,"The Touchdown dataset (Chen et al., 2019) provides instructions by human annotators for navigation through New York City streets and for resolving spatial descriptions at a given location. To enable the wider research community to work effectively with the Touchdown tasks, we are publicly releasing the 29k raw Street View panoramas needed for Touchdown. We follow the process used for the StreetLearn data release (Mirowski et al., 2019) to check panoramas for personally identifiable information and blur them as necessary. These have been added to the StreetLearn dataset and can be obtained via the same process as used previously for StreetLearn. We also provide a reference implementation for both Touchdown tasks: vision and language navigation (VLN) and spatial description resolution (SDR). We compare our model results to those given in (Chen et al., 2019) and show that the panoramas we have added to StreetLearn support both Touchdown tasks and can be used effectively for further research and comparison.",38940082,https://www.aclweb.org/anthology/2020.splu-1.7 WS-11,WS-11.10,Multilingual BERT Learns Abstract Case Representations,Isabel Papadimitriou|Ethan A. Chi|Richard Futrell|Kyle Mahowald,,38939802, -WS-11,WS-11.1093-WS11,"Parsing with Multilingual BERT, a Small Corpus, and a Small Treebank",Ethan C. Chau|Lucy H. Lin|Noah A. Smith,,38940630,https://www.aclweb.org/anthology/2020.findings-emnlp.118 +WS-11,WS-11.1093-WS11,"Parsing with Multilingual BERT, a Small Corpus, and a Small Treebank",Ethan C. Chau|Lucy H. Lin|Noah A. Smith,"Pretrained multilingual contextual representations have shown great success, but due to the limits of their pretraining data, their benefits do not apply equally to all language varieties. This presents a challenge for language varieties unfamiliar to these models, whose labeled and unlabeled data is too limited to train a monolingual model effectively. We propose the use of additional language-specific pretraining and vocabulary augmentation to adapt multilingual models to low-resource settings. Using dependency parsing of four diverse low-resource language varieties as a case study, we show that these methods significantly improve performance over baselines, especially in the lowest-resource cases, and demonstrate the importance of the relationship between such models’ pretraining data and target language varieties.",38940630,https://www.aclweb.org/anthology/2020.findings-emnlp.118 WS-11,WS-11.11,Keyword Spotting: A quick-and-dirty method for extracting typological features of language from grammatical descriptions,Harald Hammarström,,38939803, -WS-11,WS-11.12,Imputing typological values via phylogenetic inference,Gerhard Jäger,,38939793,https://www.aclweb.org/anthology/2020.sigtyp-1.5 +WS-11,WS-11.12,Imputing typological values via phylogenetic inference,Gerhard Jäger,"This paper describes a workflow to impute missing values in a typological database, a sub- set of the World Atlas of Language Structures (WALS). Using a world-wide phylogeny de- rived from lexical data, the model assumes a phylogenetic continuous time Markov chain governing the evolution of typological val- ues. Data imputation is performed via a Max- imum Likelihood estimation on the basis of this model. As back-off model for languages whose phylogenetic position is unknown, a k- nearest neighbor classification based on geo- graphic distance is performed.",38939793,https://www.aclweb.org/anthology/2020.sigtyp-1.5 WS-11,WS-11.13,DEmA: the Pavia Diachronic Emergence of Alignment database,Sonia Cristofaro|Guglielmo Inglese,,38939804, WS-11,WS-11.14,A dataset and metric to evaluate lexical extraction from parallel corpora,Barend Beekhuizen,,38939805, -WS-11,WS-11.1409,SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddings,Philipp Dufter|Masoud Jalili Sabet|‪François Yvon|‪Hinrich Schütze‬,,38940631,https://www.aclweb.org/anthology/2020.findings-emnlp.147 +WS-11,WS-11.1409,SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddings,Philipp Dufter|Masoud Jalili Sabet|‪François Yvon|‪Hinrich Schütze‬,"Word alignments are useful for tasks like statistical and neural machine translation (NMT) and cross-lingual annotation projection. Statistical word aligners perform well, as do methods that extract alignments jointly with translations in NMT. However, most approaches require parallel training data and quality decreases as less training data is available. We propose word alignment methods that require no parallel data. The key idea is to leverage multilingual word embeddings – both static and contextualized – for word alignment. Our multilingual embeddings are created from monolingual data only without relying on any parallel data or dictionaries. We find that alignments created from embeddings are superior for four and comparable for two language pairs compared to those produced by traditional statistical aligners – even with abundant parallel data; e.g., contextualized embeddings achieve a word alignment F1 for English-German that is 5 percentage points higher than eflomal, a high-quality statistical aligner, trained on 100k parallel sentences.",38940631,https://www.aclweb.org/anthology/2020.findings-emnlp.147 WS-11,WS-11.16,The role of community size and network structure in shaping linguistic diversity: experimental evidence,Limor Raviv|Antje Meyer|Shiri Lev-Ari,,38939806, WS-11,WS-11.17,NUIG: Multitasking Self-attention based approach to SigTyp 2020 Shared Task,Chinmay Choudhary|Colm O'Riordan,"The paper describes the Multitasking Self-attention based approach to constrained sub-task within Sigtyp 2020 Shared task. Our model is simple neural network based architecture inspired by Transformers (CITATION) model. The model uses Multitasking to compute values of all WALS features for a given input language simultaneously. Results show that our approach performs at par with the baseline approaches, even though our proposed approach requires only phylogenetic and geographical attributes namely Longitude, Latitude, Genus-index, Family-index and Country-index and do not use any of the known WALS features of the respective input language, to compute its missing WALS features.",38939794,https://www.aclweb.org/anthology/2020.sigtyp-1.6 -WS-11,WS-11.18,KMI-Panlingua-IITKGP @SIGTYP2020: Exploring Rules and Hybrid Systems for Automatic Prediction of Typological Features,Ritesh Kumar|Deepak Alok|Akanksha Bansal|Bornini Lahiri|Atul Kr. Ojha,,38939795,https://www.aclweb.org/anthology/2020.sigtyp-1.2 +WS-11,WS-11.18,KMI-Panlingua-IITKGP @SIGTYP2020: Exploring Rules and Hybrid Systems for Automatic Prediction of Typological Features,Ritesh Kumar|Deepak Alok|Akanksha Bansal|Bornini Lahiri|Atul Kr. Ojha,"This paper enumerates SigTyP 2020 Shared Task on the prediction of typological features as performed by the KMI-Panlingua-IITKGP team. The task entailed the prediction of missing values in a particular language, provided, the name of the language family, its genus, location (in terms of latitude and longitude coordinates and name of the country where it is spoken) and a set of feature-value pair are available. As part of fulfillment of the aforementioned task, the team submitted 3 kinds of system - 2 rule-based and one hybrid system. Of these 3, one rule-based system generated the best performance on the test set. All the systems were ‘constrained’ in the sense that no additional dataset or information, other than those provided by the organisers, was used for developing the systems.",38939795,https://www.aclweb.org/anthology/2020.sigtyp-1.2 WS-11,WS-11.2,Information from Topic Contexts: The Prediction of Aspectual Coding of Verbs in Russian,Michael Richter|Tariq Yousef,,38939796, WS-11,WS-11.3,Uncovering Typological Context-Sensitive Features,Chiara Alzetta|Felice Dell'Orletta|Simonetta Montemagni|Giulia Venturi,,38939797, -WS-11,WS-11.4,NEMO: Frequentist Inference Approach to Constrained Linguistic Typology Feature Prediction in SIGTYP 2020 Shared Task,Alexander Gutkin|Richard Sproat,,38939791,https://www.aclweb.org/anthology/2020.sigtyp-1.3 +WS-11,WS-11.4,NEMO: Frequentist Inference Approach to Constrained Linguistic Typology Feature Prediction in SIGTYP 2020 Shared Task,Alexander Gutkin|Richard Sproat,"This paper describes the NEMO submission to SIGTYP 2020 shared task (Bjerva et al., 2020) which deals with prediction of linguistic typological features for multiple languages using the data derived from World Atlas of Language Structures (WALS). We employ frequentist inference to represent correlations between typological features and use this representation to train simple multi-class estimators that predict individual features. We describe two submitted ridge regression-based configurations which ranked second and third overall in the constrained task. Our best configuration achieved the microaveraged accuracy score of 0.66 on 149 test languages.",38939791,https://www.aclweb.org/anthology/2020.sigtyp-1.3 WS-11,WS-11.5,Towards Induction of Structured Phoneme Inventories,Alexander Gutkin|Martin Jansche|Lucy Skidmore,,38939798, WS-11,WS-11.6,Is Typology-Based Adaptation Effective for Multilingual Sequence Labelling?,Ahmet Üstün|Arianna Bisazza|Gosse Bouma|Gertjan Van Noord,,38939799, -WS-11,WS-11.7,Predicting Typological Features inWALS using Language Embeddings and Conditional Probabilities: ÚFAL Submission to the SIGTYP 2020 Shared Task,Martin Vastl|Daniel Zeman|Rudolf Rosa,,38939792,https://www.aclweb.org/anthology/2020.sigtyp-1.4 +WS-11,WS-11.7,Predicting Typological Features inWALS using Language Embeddings and Conditional Probabilities: ÚFAL Submission to the SIGTYP 2020 Shared Task,Martin Vastl|Daniel Zeman|Rudolf Rosa,"We present our submission to the SIGTYP 2020 Shared Task on the prediction of typological features. We submit a constrained system, predicting typological features only based on the WALS database. We investigate two approaches. The simpler of the two is a system based on estimating correlation of feature values within languages by computing conditional probabilities and mutual information. The second approach is to train a neural predictor operating on precomputed language embeddings based on WALS features. Our submitted system combines the two approaches based on their self-estimated confidence scores. We reach the accuracy of 70.7% on the test data and rank first in the shared task.",38939792,https://www.aclweb.org/anthology/2020.sigtyp-1.4 WS-11,WS-11.8,SNACS Annotation of Case Markers and Adpositions in Hindi,Aryaman Arora|Nathan Schneider,,38939800, -WS-11,WS-11.816,Cross-lingual Alignment Methods for Multilingual BERT: A Comparative Study,Saurabh Kulshreshtha|‪José Luis Redondo García|Ching-Yun Chang,,38940629,https://www.aclweb.org/anthology/2020.findings-emnlp.83 +WS-11,WS-11.816,Cross-lingual Alignment Methods for Multilingual BERT: A Comparative Study,Saurabh Kulshreshtha|‪José Luis Redondo García|Ching-Yun Chang,"Multilingual BERT (mBERT) has shown reasonable capability for zero-shot cross-lingual transfer when fine-tuned on downstream tasks. Since mBERT is not pre-trained with explicit cross-lingual supervision, transfer performance can further be improved by aligning mBERT with cross-lingual signal. Prior work propose several approaches to align contextualised embeddings. In this paper we analyse how different forms of cross-lingual supervision and various alignment methods influence the transfer capability of mBERT in zero-shot setting. Specifically, we compare parallel corpora vs dictionary-based supervision and rotational vs fine-tuning based alignment methods. We evaluate the performance of different alignment methodologies across eight languages on two tasks: Name Entity Recognition and Semantic Slot Filling. In addition, we propose a novel normalisation method which consistently improves the performance of rotation-based alignment including a notable 3% F1 improvement for distant and typologically dissimilar languages. Importantly we identify the biases of the alignment methods to the type of task and proximity to the transfer language. We also find that supervision from parallel corpus is generally superior to dictionary alignments.",38940629,https://www.aclweb.org/anthology/2020.findings-emnlp.83 WS-11,WS-11.9,Multilingual Jointly Trained Acoustic and Written Word Embeddings,Yushi Hu|Shane Settle|Karen Livescu,,38939801, -WS-12,WS-12.1,Dilated Convolutional Attention Network for Medical Code Assignment from Clinical Text,Shaoxiong Ji|Erik Cambria|Pekka Marttinen,,38939807,https://www.aclweb.org/anthology/2020.clinicalnlp-1.8 -WS-12,WS-12.10,Information Extraction from Swedish Medical Prescriptions with Sig-Transformer Encoder,John Pougué Biyong|Bo Wang|Terry Lyons|Alejo Nevado-Holgado,,38939813,https://www.aclweb.org/anthology/2020.clinicalnlp-1.5 -WS-12,WS-12.1041,Learning to Generate Clinically Coherent Chest X-Ray Reports,Justin Lovelace|Bobak Mortazavi,,38940177,https://www.aclweb.org/anthology/2020.findings-emnlp.110 -WS-12,WS-12.11,How You Ask Matters: The Effect of Paraphrastic Questions to BERT Performance on a Clinical SQuAD Dataset,Sungrim,,38939814,https://www.aclweb.org/anthology/2020.clinicalnlp-1.13 -WS-12,WS-12.12,Multiple Sclerosis Severity Classification From Clinical Text,Alister D'Costa|Stefan Denkovski|Michal Malyska|Sae Young Moon|Brandon Rufino|Zhen Yang|Taylor Killian|Marzyeh Ghassemi,,38939815,https://www.aclweb.org/anthology/2020.clinicalnlp-1.2 -WS-12,WS-12.13,An Ensemble Approach to Automatic Structuring of Radiology Reports,Morteza Pourreza Shahri|Amir Tahmasebi|Bingyang Ye|Henghui Zhu|Javed Aslam|Timothy Ferris,,38939816,https://www.aclweb.org/anthology/2020.clinicalnlp-1.28 -WS-12,WS-12.15,Various Levels of Representation for Predicting Stroke Prognosis using Text Records of Magnetic Resonance Imaging,Tak-Sung Heo|Chulho Kim|Jeong-Myeong Choi|Yeong-Seok Jeong|Yu-Seop Kim,,38939817,https://www.aclweb.org/anthology/2020.clinicalnlp-1.1 -WS-12,WS-12.16,Relative and Incomplete Time Expression Anchoring for Clinical Text,Louise Dupuis|Nicol Bergou|Hegler Tissot|Sumithra Velupillai,,38939818,https://www.aclweb.org/anthology/2020.clinicalnlp-1.14 -WS-12,WS-12.1640,Learning Visual-Semantic Embeddings for Reporting Abnormal Findings on Chest X-rays,Jianmo Ni|Chun-Nan Hsu|Amilcare Gentili|Julian McAuley,,38940178,https://www.aclweb.org/anthology/2020.findings-emnlp.176 -WS-12,WS-12.17,MeDAL: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining,Zhi Wen|Xing Han Lu|Siva Reddy,,38939819,https://www.aclweb.org/anthology/2020.clinicalnlp-1.15 -WS-12,WS-12.1713,Characterizing the Value of Information in Medical Notes,Chao-Chun Hsu|Shantanu Karnwal|Sendhil Mullainathan|Ziad Obermeyer|Chenhao Tan,,38940179,https://www.aclweb.org/anthology/2020.findings-emnlp.187 -WS-12,WS-12.18,Evaluation of Transfer Learning for Adverse Drug Event (ADE) and Medication Entity Extraction,Sankaran Narayanan|Kaivalya Mannam|Sreeranga P Rajan|P Venkat Rangan,,38939820,https://www.aclweb.org/anthology/2020.clinicalnlp-1.6 +WS-12,WS-12.1,Dilated Convolutional Attention Network for Medical Code Assignment from Clinical Text,Shaoxiong Ji|Erik Cambria|Pekka Marttinen,"Medical code assignment, which predicts medical codes from clinical texts, is a fundamental task of intelligent medical information systems. The emergence of deep models in natural language processing has boosted the development of automatic assignment methods. However, recent advanced neural architectures with flat convolutions or multi-channel feature concatenation ignore the sequential causal constraint within a text sequence and may not learn meaningful clinical text representations, especially for lengthy clinical notes with long-term sequential dependency. This paper proposes a Dilated Convolutional Attention Network (DCAN), integrating dilated convolutions, residual connections, and label attention, for medical code assignment. It adopts dilated convolutions to capture complex medical patterns with a receptive field which increases exponentially with dilation size. Experiments on a real-world clinical dataset empirically show that our model improves the state of the art.",38939807,https://www.aclweb.org/anthology/2020.clinicalnlp-1.8 +WS-12,WS-12.10,Information Extraction from Swedish Medical Prescriptions with Sig-Transformer Encoder,John Pougué Biyong|Bo Wang|Terry Lyons|Alejo Nevado-Holgado,"Relying on large pretrained language models such as Bidirectional Encoder Representations from Transformers (BERT) for encoding and adding a simple prediction layer has led to impressive performance in many clinical natural language processing (NLP) tasks. In this work, we present a novel extension to the Transformer architecture, by incorporating signature transform with the self-attention model. This architecture is added between embedding and prediction layers. Experiments on a new Swedish prescription data show the proposed architecture to be superior in two of the three information extraction tasks, comparing to baseline models. Finally, we evaluate two different embedding approaches between applying Multilingual BERT and translating the Swedish text to English then encode with a BERT model pretrained on clinical notes.",38939813,https://www.aclweb.org/anthology/2020.clinicalnlp-1.5 +WS-12,WS-12.1041,Learning to Generate Clinically Coherent Chest X-Ray Reports,Justin Lovelace|Bobak Mortazavi,"Automated radiology report generation has the potential to reduce the time clinicians spend manually reviewing radiographs and streamline clinical care. However, past work has shown that typical abstractive methods tend to produce fluent, but clinically incorrect radiology reports. In this work, we develop a radiology report generation model utilizing the transformer architecture that produces superior reports as measured by both standard language generation and clinical coherence metrics compared to competitive baselines. We then develop a method to differentiably extract clinical information from generated reports and utilize this differentiability to fine-tune our model to produce more clinically coherent reports.",38940177,https://www.aclweb.org/anthology/2020.findings-emnlp.110 +WS-12,WS-12.11,How You Ask Matters: The Effect of Paraphrastic Questions to BERT Performance on a Clinical SQuAD Dataset,Sungrim,"Reading comprehension style question-answering (QA) based on patient-specific documents represents a growing area in clinical NLP with plentiful applications. Bidirectional Encoder Representations from Transformers (BERT) and its derivatives lead the state-of-the-art accuracy on the task, but most evaluation has treated the data as a pre-mixture without systematically looking into the potential effect of imperfect train/test questions. The current study seeks to address this gap by experimenting with full versus partial train/test data consisting of paraphrastic questions. Our key findings include 1) training with all pooled question variants yielded best accuracy, 2) the accuracy varied widely, from 0.74 to 0.80, when trained with each single question variant, and 3) questions of similar lexical/syntactic structure tended to induce identical answers. The results suggest that how you ask questions matters in BERT-based QA, especially at the training stage.",38939814,https://www.aclweb.org/anthology/2020.clinicalnlp-1.13 +WS-12,WS-12.12,Multiple Sclerosis Severity Classification From Clinical Text,Alister D'Costa|Stefan Denkovski|Michal Malyska|Sae Young Moon|Brandon Rufino|Zhen Yang|Taylor Killian|Marzyeh Ghassemi,"Multiple Sclerosis (MS) is a chronic, inflammatory and degenerative neurological disease, which is monitored by a specialist using the Expanded Disability Status Scale (EDSS) and recorded in unstructured text in the form of a neurology consult note. An EDSS measurement contains an overall ‘EDSS’ score and several functional subscores. Typically, expert knowledge is required to interpret consult notes and generate these scores. Previous approaches used limited context length Word2Vec embeddings and keyword searches to predict scores given a consult note, but often failed when scores were not explicitly stated. In this work, we present MS-BERT, the first publicly available transformer model trained on real clinical data other than MIMIC. Next, we present MSBC, a classifier that applies MS-BERT to generate embeddings and predict EDSS and functional subscores. Lastly, we explore combining MSBC with other models through the use of Snorkel to generate scores for unlabelled consult notes. MSBC achieves state-of-the-art performance on all metrics and prediction tasks and outperforms the models generated from the Snorkel ensemble. We improve Macro-F1 by 0.12 (to 0.88) for predicting EDSS and on average by 0.29 (to 0.63) for predicting functional subscores over previous Word2Vec CNN and rule-based approaches.",38939815,https://www.aclweb.org/anthology/2020.clinicalnlp-1.2 +WS-12,WS-12.13,An Ensemble Approach to Automatic Structuring of Radiology Reports,Morteza Pourreza Shahri|Amir Tahmasebi|Bingyang Ye|Henghui Zhu|Javed Aslam|Timothy Ferris,"Automatic structuring of electronic medical records is of high demand for clinical workflow solutions to facilitate extraction, storage, and querying of patient care information. However, developing a scalable solution is extremely challenging, specifically for radiology reports, as most healthcare institutes use either no template or department/institute specific templates. Moreover, radiologists’ reporting style varies from one to another as sentences are written in a telegraphic format and do not follow general English grammar rules. In this work, we present an ensemble method that consolidates the predictions of three models, capturing various attributes of textual information for automatic labeling of sentences with section labels. These three models are: 1) Focus Sentence model, capturing context of the target sentence; 2) Surrounding Context model, capturing the neighboring context of the target sentence; and finally, 3) Formatting/Layout model, aimed at learning report formatting cues. We utilize Bi-directional LSTMs, followed by sentence encoders, to acquire the context. Furthermore, we define several features that incorporate the structure of reports. We compare our proposed approach against multiple baselines and state-of-the-art approaches on a proprietary dataset as well as 100 manually annotated radiology notes from the MIMIC-III dataset, which we are making publicly available. Our proposed approach significantly outperforms other approaches by achieving 97.1% accuracy.",38939816,https://www.aclweb.org/anthology/2020.clinicalnlp-1.28 +WS-12,WS-12.15,Various Levels of Representation for Predicting Stroke Prognosis using Text Records of Magnetic Resonance Imaging,Tak-Sung Heo|Chulho Kim|Jeong-Myeong Choi|Yeong-Seok Jeong|Yu-Seop Kim,"Stroke is one of the leading causes of death and disability worldwide. Stroke is treatable, but it is prone to disability after treatment and must be prevented. To grasp the degree of disability caused by stroke, we use magnetic resonance imaging text records to predict stroke and measure the performance according to the document-level and sentence-level representation. As a result of the experiment, the document-level representation shows better performance.",38939817,https://www.aclweb.org/anthology/2020.clinicalnlp-1.1 +WS-12,WS-12.16,Relative and Incomplete Time Expression Anchoring for Clinical Text,Louise Dupuis|Nicol Bergou|Hegler Tissot|Sumithra Velupillai,"Extracting and modeling temporal information in clinical text is an important element for developing timelines and disease trajectories. Time information in written text varies in preciseness and explicitness, posing challenges for NLP approaches that aim to accurately anchor temporal information on a timeline. Relative and incomplete time expressions (RI-Timexes) are expressions that require additional information for their temporal anchor to be resolved, but few studies have addressed this challenge specifically. In this study, we aimed to reproduce and verify a classification approach for identifying anchor dates and relations in clinical text, and propose a novel relation classification approach for this task.",38939818,https://www.aclweb.org/anthology/2020.clinicalnlp-1.14 +WS-12,WS-12.1640,Learning Visual-Semantic Embeddings for Reporting Abnormal Findings on Chest X-rays,Jianmo Ni|Chun-Nan Hsu|Amilcare Gentili|Julian McAuley,"Automatic medical image report generation has drawn growing attention due to its potential to alleviate radiologists’ workload. Existing work on report generation often trains encoder-decoder networks to generate complete reports. However, such models are affected by data bias (e.g. label imbalance) and face common issues inherent in text generation models (e.g. repetition). In this work, we focus on reporting abnormal findings on radiology images; instead of training on complete radiology reports, we propose a method to identify abnormal findings from the reports in addition to grouping them with unsupervised clustering and minimal rules. We formulate the task as cross-modal retrieval and propose Conditional Visual-Semantic Embeddings to align images and fine-grained abnormal findings in a joint embedding space. We demonstrate that our method is able to retrieve abnormal findings and outperforms existing generation models on both clinical correctness and text generation metrics.",38940178,https://www.aclweb.org/anthology/2020.findings-emnlp.176 +WS-12,WS-12.17,MeDAL: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining,Zhi Wen|Xing Han Lu|Siva Reddy,"One of the biggest challenges that prohibit the use of many current NLP methods in clinical settings is the availability of public datasets. In this work, we present MeDAL, a large medical text dataset curated for abbreviation disambiguation, designed for natural language understanding pre-training in the medical domain. We pre-trained several models of common architectures on this dataset and empirically showed that such pre-training leads to improved performance and convergence speed when fine-tuning on downstream medical tasks.",38939819,https://www.aclweb.org/anthology/2020.clinicalnlp-1.15 +WS-12,WS-12.1713,Characterizing the Value of Information in Medical Notes,Chao-Chun Hsu|Shantanu Karnwal|Sendhil Mullainathan|Ziad Obermeyer|Chenhao Tan,"Machine learning models depend on the quality of input data. As electronic health records are widely adopted, the amount of data in health care is growing, along with complaints about the quality of medical notes. We use two prediction tasks, readmission prediction and in-hospital mortality prediction, to characterize the value of information in medical notes. We show that as a whole, medical notes only provide additional predictive power over structured information in readmission prediction. We further propose a probing framework to select parts of notes that enable more accurate predictions than using all notes, despite that the selected information leads to a distribution shift from the training data (“all notes”). Finally, we demonstrate that models trained on the selected valuable information achieve even better predictive performance, with only 6.8%of all the tokens for readmission prediction.",38940179,https://www.aclweb.org/anthology/2020.findings-emnlp.187 +WS-12,WS-12.18,Evaluation of Transfer Learning for Adverse Drug Event (ADE) and Medication Entity Extraction,Sankaran Narayanan|Kaivalya Mannam|Sreeranga P Rajan|P Venkat Rangan,"We evaluate several biomedical contextual embeddings (based on BERT, ELMo, and Flair) for the detection of medication entities such as Drugs and Adverse Drug Events (ADE) from Electronic Health Records (EHR) using the 2018 ADE and Medication Extraction (Track 2) n2c2 data-set. We identify best practices for transfer learning, such as language-model fine-tuning and scalar mix. Our transfer learning models achieve strong performance in the overall task (F1=92.91%) as well as in ADE identification (F1=53.08%). Flair-based embeddings out-perform in the identification of context-dependent entities such as ADE. BERT-based embeddings out-perform in recognizing clinical terminology such as Drug and Form entities. ELMo-based embeddings deliver competitive performance in all entities. We develop a sentence-augmentation method for enhanced ADE identification benefiting BERT-based and ELMo-based models by up to 3.13% in F1 gains. Finally, we show that a simple ensemble of these models out-paces most current methods in ADE extraction (F1=55.77%).",38939820,https://www.aclweb.org/anthology/2020.clinicalnlp-1.6 WS-12,WS-12.19,"Knowledge Grounded Conversational Symptom Detection - with Graph Memory Networks",Hongyin Luo|Shang-Wen Li|James Glass,,38939821,https://www.aclweb.org/anthology/2020.clinicalnlp-1.16 -WS-12,WS-12.20,Pretrained Language Models for Biomedical and Clinical Tasks: Understanding and Extending the State-of-the-Art,Patrick Lewis|Myle Ott|Jingfei Du|Veselin Stoyanov,,38939822,https://www.aclweb.org/anthology/2020.clinicalnlp-1.17 -WS-12,WS-12.21,Incorporating Risk Factor Embeddings in Pre-trained Transformers Improves Sentiment Prediction in Psychiatric Discharge Summaries,Xiyu Ding|Mei-Hua Hall|Timothy Miller,,38939823,https://www.aclweb.org/anthology/2020.clinicalnlp-1.4 -WS-12,WS-12.2127,PharmMT: A Neural Machine Translation Approach to Simplify Prescription Directions,Jiazhao Li|Corey Lester|Xinyan Zhao|Yuting Ding|Yun Jiang|V.G.Vinod Vydiswaran,,38940180,https://www.aclweb.org/anthology/2020.findings-emnlp.251 -WS-12,WS-12.23,Assessment of DistilBERT performance on Named Entity Recognition task for the detection of Protected Health Information and medical concepts,Macarious Abadeer,,38939824,https://www.aclweb.org/anthology/2020.clinicalnlp-1.18 + with Graph Memory Networks",Hongyin Luo|Shang-Wen Li|James Glass,"In this work, we propose a novel goal-oriented dialog task, automatic symptom detection. We build a system that can interact with patients through dialog to detect and collect clinical symptoms automatically, which can save a doctor’s time interviewing the patient. Given a set of explicit symptoms provided by the patient to initiate a dialog for diagnosing, the system is trained to collect implicit symptoms by asking questions, in order to collect more information for making an accurate diagnosis. After getting the reply from the patient for each question, the system also decides whether current information is enough for a human doctor to make a diagnosis. To achieve this goal, we propose two neural models and a training pipeline for the multi-step reasoning task. We also build a knowledge graph as additional inputs to further improve model performance. Experiments show that our model significantly outperforms the baseline by 4%, discovering 67% of implicit symptoms on average with a limited number of questions.",38939821,https://www.aclweb.org/anthology/2020.clinicalnlp-1.16 +WS-12,WS-12.20,Pretrained Language Models for Biomedical and Clinical Tasks: Understanding and Extending the State-of-the-Art,Patrick Lewis|Myle Ott|Jingfei Du|Veselin Stoyanov,"A large array of pretrained models are available to the biomedical NLP (BioNLP) community. Finding the best model for a particular task can be difficult and time-consuming. For many applications in the biomedical and clinical domains, it is crucial that models can be built quickly and are highly accurate. We present a large-scale study across 18 established biomedical and clinical NLP tasks to determine which of several popular open-source biomedical and clinical NLP models work well in different settings. Furthermore, we apply recent advances in pretraining to train new biomedical language models, and carefully investigate the effect of various design choices on downstream performance. Our best models perform well in all of our benchmarks, and set new State-of-the-Art in 9 tasks. We release these models in the hope that they can help the community to speed up and increase the accuracy of BioNLP and text mining applications.",38939822,https://www.aclweb.org/anthology/2020.clinicalnlp-1.17 +WS-12,WS-12.21,Incorporating Risk Factor Embeddings in Pre-trained Transformers Improves Sentiment Prediction in Psychiatric Discharge Summaries,Xiyu Ding|Mei-Hua Hall|Timothy Miller,"Reducing rates of early hospital readmission has been recognized and identified as a key to improve quality of care and reduce costs. There are a number of risk factors that have been hypothesized to be important for understanding re-admission risk, including such factors as problems with substance abuse, ability to maintain work, relations with family. In this work, we develop Roberta-based models to predict the sentiment of sentences describing readmission risk factors in discharge summaries of patients with psychosis. We improve substantially on previous results by a scheme that shares information across risk factors while also allowing the model to learn risk factor-specific information.",38939823,https://www.aclweb.org/anthology/2020.clinicalnlp-1.4 +WS-12,WS-12.2127,PharmMT: A Neural Machine Translation Approach to Simplify Prescription Directions,Jiazhao Li|Corey Lester|Xinyan Zhao|Yuting Ding|Yun Jiang|V.G.Vinod Vydiswaran,"The language used by physicians and health professionals in prescription directions includes medical jargon and implicit directives and causes much confusion among patients. Human intervention to simplify the language at the pharmacies may introduce additional errors that can lead to potentially severe health outcomes. We propose a novel machine translation-based approach, PharmMT, to automatically and reliably simplify prescription directions into patient-friendly language, thereby significantly reducing pharmacist workload. We evaluate the proposed approach over a dataset consisting of over 530K prescriptions obtained from a large mail-order pharmacy. The end-to-end system achieves a BLEU score of 60.27 against the reference directions generated by pharmacists, a 39.6% relative improvement over the rule-based normalization. Pharmacists judged 94.3% of the simplified directions as usable as-is or with minimal changes. This work demonstrates the feasibility of a machine translation-based tool for simplifying prescription directions in real-life.",38940180,https://www.aclweb.org/anthology/2020.findings-emnlp.251 +WS-12,WS-12.23,Assessment of DistilBERT performance on Named Entity Recognition task for the detection of Protected Health Information and medical concepts,Macarious Abadeer,"Bidirectional Encoder Representations from Transformers (BERT) models achieve state-of-the-art performance on a number of Natural Language Processing tasks. However, their model size on disk often exceeds 1 GB and the process of fine-tuning them and using them to run inference consumes significant hardware resources and runtime. This makes them hard to deploy to production environments. This paper fine-tunes DistilBERT, a lightweight deep learning model, on medical text for the named entity recognition task of Protected Health Information (PHI) and medical concepts. This work provides a full assessment of the performance of DistilBERT in comparison with BERT models that were pre-trained on medical text. For Named Entity Recognition task of PHI, DistilBERT achieved almost the same results as medical versions of BERT in terms of F1 score at almost half the runtime and consuming approximately half the disk space. On the other hand, for the detection of medical concepts, DistilBERT’s F1 score was lower by 4 points on average than medical BERT variants.",38939824,https://www.aclweb.org/anthology/2020.clinicalnlp-1.18 WS-12,WS-12.25,Distinguishing between Dementia with Lewy bodies (DLB) and Alzheimer's Disease (AD) using Mental Health Records: a Classification Approach,Zixu Wang|Julia Ive|Sinead Moylett|Christoph Mueller|Rudolf Cardinal|Sumithra Velupillai|John O'Brien|Robert Stewart,,38939825, -WS-12,WS-12.2509,A Dual-Attention Network for Joint Named Entity Recognition and Sentence Classification of Adverse Drug Events,Susmitha Wunnava|Xiao Qin|Tabassum Kakar|Xiangnan Kong|Elke A. Rundensteiner,,38940181,https://www.aclweb.org/anthology/2020.findings-emnlp.306 -WS-12,WS-12.26,Weakly Supervised Medication Regimen Extraction from Medical Conversations,Dhruvesh Patel|Sandeep Konam|Sai Prabhakar,,38939826,https://www.aclweb.org/anthology/2020.clinicalnlp-1.20 -WS-12,WS-12.27,Extracting Relations between Radiotherapy Treatment Details,Danielle Bitterman|Timothy Miller|David Harris|Chen Lin|Sean Finan|Jeremy Warner|Raymond Mak|Guergana Savova,,38939827,https://www.aclweb.org/anthology/2020.clinicalnlp-1.21 -WS-12,WS-12.2801,Dr. Summarize: Global Summarization of Medical Dialogue by Exploiting Local Structures,Anirudh Joshi|Namit Katariya|Xavier Amatriain|Anitha Kannan,,38940182,https://www.aclweb.org/anthology/2020.findings-emnlp.335 -WS-12,WS-12.2804,Generating Accurate Electronic Health Assessment from Medical Graph,Zhichao Yang|Hong Yu,,38940183,https://www.aclweb.org/anthology/2020.findings-emnlp.336 -WS-12,WS-12.29,The Chilean Waiting List Corpus: a new resource for clinical Named Entity Recognition in Spanish,Pablo Báez|Fabián Villena|Matías Rojas|Manuel Durán|Jocelyn Dunstan,,38939828,https://www.aclweb.org/anthology/2020.clinicalnlp-1.32 -WS-12,WS-12.3,Classification of Syncope Cases in Norwegian Medical Records,Ildiko Pilan|Pål H. Brekke|Fredrik A. Dahl|Tore Gundersen|Haldor Husby|Øystein Nytrø|Lilja Øvrelid,,38939808,https://www.aclweb.org/anthology/2020.clinicalnlp-1.9 -WS-12,WS-12.30,BioBERTpt - A Portuguese Neural Language Model for Clinical Named Entity Recognition,Elisa Terumi Rubel Schneider|João Vitor Andrioli De Souza|Julien Knafou|Lucas Emanuel Silva E Oliveira|Jenny Copara|Yohan Bonescki Gumiel|Lucas Ferro Antunes De Oliveira|Emerson Cabrera Paraiso|Douglas Teodoro|Cláudia Maria Cabral Moro Barra,,38939829,https://www.aclweb.org/anthology/2020.clinicalnlp-1.7 -WS-12,WS-12.31,Cancer Registry Information Extraction via Transfer Learning,Yan-Jie Lin|Hong-Jie Dai|You-Chen Zhang|Chung-Yang Wu|Yu-Cheng Chang|Pin-Jou Lu|Chih-Jen Huang|Yu-Tsang Wang|Hui-Min Hsieh|Kun-San Chao|Tsang-Wu Liu|I-Shou Chang|Yi-Hsin Connie Yang|Ti-Hao Wang|Ko-Jiunn Liu|Li-Tzong Chen|Sheau-Fang Yang,,38939830,https://www.aclweb.org/anthology/2020.clinicalnlp-1.22 -WS-12,WS-12.33,Utilizing Multimodal Feature Consistency to Detect Adversarial Examples on Clinical Summaries,Wenjie Wang|Youngja Park|Taesung Lee|Ian Molloy|Pengfei Tang|Li Xiong,,38939831,https://www.aclweb.org/anthology/2020.clinicalnlp-1.29 -WS-12,WS-12.37,PHICON: Improving Generalization of Clinical Text De-identification Models via Data Augmentation,Xiang Yue|Shuang Zhou,,38939832,https://www.aclweb.org/anthology/2020.clinicalnlp-1.23 -WS-12,WS-12.38,Where's the Question? A Multi-channel Deep Convolutional Neural Network for Question Identification in Textual Data,George Michalopoulos|Helen Chen|Alexander Wong,,38939833,https://www.aclweb.org/anthology/2020.clinicalnlp-1.24 -WS-12,WS-12.39,Advancing Seq2seq with Joint Paraphrase Learning,So Yeon Min|Preethi Raghavan|Peter Szolovits,,38939834,https://www.aclweb.org/anthology/2020.clinicalnlp-1.30 -WS-12,WS-12.4,Comparison of Machine Learning Methods for Multi-label Classificationof Nursing Education and Licensure Exam Questions,John Langton|Krishna Srihasam|Junlin Jiang,,38939809,https://www.aclweb.org/anthology/2020.clinicalnlp-1.10 -WS-12,WS-12.40,Learning from Unlabelled Data for Clinical Semantic Textual Similarity,Yuxia Wang|Karin Verspoor|Timothy Baldwin,,38939835,https://www.aclweb.org/anthology/2020.clinicalnlp-1.25 -WS-12,WS-12.43,BERT-XML: Large Scale Automated ICD Coding Using BERTPretraining,Zachariah Zhang|Jingshu Liu|Narges Razavian,,38939836,https://www.aclweb.org/anthology/2020.clinicalnlp-1.3 -WS-12,WS-12.44,Joint Learning with Pre-trained Transformer on Named Entity Recognition and Relation Extraction Tasks for Clinical Analytics,Miao Chen|Ganhui Lan|Fang Du|Victor Lobanov,,38939837,https://www.aclweb.org/anthology/2020.clinicalnlp-1.26 +WS-12,WS-12.2509,A Dual-Attention Network for Joint Named Entity Recognition and Sentence Classification of Adverse Drug Events,Susmitha Wunnava|Xiao Qin|Tabassum Kakar|Xiangnan Kong|Elke A. Rundensteiner,"An adverse drug event (ADE) is an injury resulting from medical intervention related to a drug. Automatic ADE detection from text is either fine-grained (ADE entity recognition) or coarse-grained (ADE assertive sentence classification), with limited efforts leveraging inter-dependencies among the two granularities. We instead propose a multi-grained joint deep network to concurrently learn the ADE entity recognition and ADE sentence classification tasks. Our joint approach takes advantage of their symbiotic relationship, with a transfer of knowledge between the two levels of granularity. Our dual-attention mechanism constructs multiple distinct representations of a sentence that capture both task-specific and semantic information in the sentence, providing stronger emphasis on the key elements essential for sentence classification. Our model improves state-of- art F1-score for both tasks: (i) entity recognition of ADE words (12.5% increase) and (ii) ADE sentence classification (13.6% increase) on MADE 1.0 benchmark of EHR notes.",38940181,https://www.aclweb.org/anthology/2020.findings-emnlp.306 +WS-12,WS-12.26,Weakly Supervised Medication Regimen Extraction from Medical Conversations,Dhruvesh Patel|Sandeep Konam|Sai Prabhakar,"Automated Medication Regimen (MR) extraction from medical conversations can not only improve recall and help patients follow through with their care plan, but also reduce the documentation burden for doctors. In this paper, we focus on extracting spans for frequency, route and change, corresponding to medications discussed in the conversation. We first describe a unique dataset of annotated doctor-patient conversations and then present a weakly supervised model architecture that can perform span extraction using noisy classification data. The model utilizes an attention bottleneck inside a classification model to perform the extraction. We experiment with several variants of attention scoring and projection functions and propose a novel transformer-based attention scoring function (TAScore). The proposed combination of TAScore and Fusedmax projection achieves a 10 point increase in Longest Common Substring F1 compared to the baseline of additive scoring plus softmax projection.",38939826,https://www.aclweb.org/anthology/2020.clinicalnlp-1.20 +WS-12,WS-12.27,Extracting Relations between Radiotherapy Treatment Details,Danielle Bitterman|Timothy Miller|David Harris|Chen Lin|Sean Finan|Jeremy Warner|Raymond Mak|Guergana Savova,"We present work on extraction of radiotherapy treatment information from the clinical narrative in the electronic medical records. Radiotherapy is a central component of the treatment of most solid cancers. Its details are described in non-standardized fashions using jargon not found in other medical specialties, complicating the already difficult task of manual data extraction. We examine the performance of several state-of-the-art neural methods for relation extraction of radiotherapy treatment details, with a goal of automating detailed information extraction. The neural systems perform at 0.82-0.88 macro-average F1, which approximates or in some cases exceeds the inter-annotator agreement. To the best of our knowledge, this is the first effort to develop models for radiotherapy relation extraction and one of the few efforts for relation extraction to describe cancer treatment in general.",38939827,https://www.aclweb.org/anthology/2020.clinicalnlp-1.21 +WS-12,WS-12.2801,Dr. Summarize: Global Summarization of Medical Dialogue by Exploiting Local Structures,Anirudh Joshi|Namit Katariya|Xavier Amatriain|Anitha Kannan,"Understanding a medical conversation between a patient and a physician poses unique natural language understanding challenge since it combines elements of standard open-ended conversation with very domain-specific elements that require expertise and medical knowledge. Summarization of medical conversations is a particularly important aspect of medical conversation understanding since it addresses a very real need in medical practice: capturing the most important aspects of a medical encounter so that they can be used for medical decision making and subsequent follow ups. In this paper we present a novel approach to medical conversation summarization that leverages the unique and independent local structures created when gathering a patient’s medical history. Our approach is a variation of the pointer generator network where we introduce a penalty on the generator distribution, and we explicitly model negations. The model also captures important properties of medical conversations such as medical knowledge coming from standardized medical ontologies better than when those concepts are introduced explicitly. Through evaluation by doctors, we show that our approach is preferred on twice the number of summaries to the baseline pointer generator model and captures most or all of the information in 80% of the conversations making it a realistic alternative to costly manual summarization by medical experts.",38940182,https://www.aclweb.org/anthology/2020.findings-emnlp.335 +WS-12,WS-12.2804,Generating Accurate Electronic Health Assessment from Medical Graph,Zhichao Yang|Hong Yu,"One of the fundamental goals of artificial intelligence is to build computer-based expert systems. Inferring clinical diagnoses to generate a clinical assessment during a patient encounter is a crucial step towards building a medical diagnostic system. Previous works were mainly based on either medical domain-specific knowledge, or patients’ prior diagnoses and clinical encounters. In this paper, we propose a novel model for automated clinical assessment generation (MCAG). MCAG is built on an innovative graph neural network, where rich clinical knowledge is incorporated into an end-to-end corpus-learning system. Our evaluation results against physician generated gold standard show that MCAG significantly improves the BLEU and rouge score compared with competitive baseline models. Further, physicians’ evaluation showed that MCAG could generate high-quality assessments.",38940183,https://www.aclweb.org/anthology/2020.findings-emnlp.336 +WS-12,WS-12.29,The Chilean Waiting List Corpus: a new resource for clinical Named Entity Recognition in Spanish,Pablo Báez|Fabián Villena|Matías Rojas|Manuel Durán|Jocelyn Dunstan,"In this work we describe the Waiting List Corpus consisting of de-identified referrals for several specialty consultations from the waiting list in Chilean public hospitals. A subset of 900 referrals was manually annotated with 9,029 entities, 385 attributes, and 284 pairs of relations with clinical relevance. A trained medical doctor annotated these referrals, and then together with other three researchers, consolidated each of the annotations. The annotated corpus has nested entities, with 32.2% of entities embedded in other entities. We use this annotated corpus to obtain preliminary results for Named Entity Recognition (NER). The best results were achieved by using a biLSTM-CRF architecture using word embeddings trained over Spanish Wikipedia together with clinical embeddings computed by the group. NER models applied to this corpus can leverage statistics of diseases and pending procedures within this waiting list. This work constitutes the first annotated corpus using clinical narratives from Chile, and one of the few for the Spanish language. The annotated corpus, the clinical word embeddings, and the annotation guidelines are freely released to the research community.",38939828,https://www.aclweb.org/anthology/2020.clinicalnlp-1.32 +WS-12,WS-12.3,Classification of Syncope Cases in Norwegian Medical Records,Ildiko Pilan|Pål H. Brekke|Fredrik A. Dahl|Tore Gundersen|Haldor Husby|Øystein Nytrø|Lilja Øvrelid,"Loss of consciousness, so-called syncope, is a commonly occurring symptom associated with worse prognosis for a number of heart-related diseases. We present a comparison of methods for a diagnosis classification task in Norwegian clinical notes, targeting syncope, i.e. fainting cases. We find that an often neglected baseline with keyword matching constitutes a rather strong basis, but more advanced methods do offer some improvement in classification performance, especially a convolutional neural network model. The developed pipeline is planned to be used for quantifying unregistered syncope cases in Norway.",38939808,https://www.aclweb.org/anthology/2020.clinicalnlp-1.9 +WS-12,WS-12.30,BioBERTpt - A Portuguese Neural Language Model for Clinical Named Entity Recognition,Elisa Terumi Rubel Schneider|João Vitor Andrioli De Souza|Julien Knafou|Lucas Emanuel Silva E Oliveira|Jenny Copara|Yohan Bonescki Gumiel|Lucas Ferro Antunes De Oliveira|Emerson Cabrera Paraiso|Douglas Teodoro|Cláudia Maria Cabral Moro Barra,"With the growing number of electronic health record data, clinical NLP tasks have become increasingly relevant to unlock valuable information from unstructured clinical text. Although the performance of downstream NLP tasks, such as named-entity recognition (NER), in English corpus has recently improved by contextualised language models, less research is available for clinical texts in low resource languages. Our goal is to assess a deep contextual embedding model for Portuguese, so called BioBERTpt, to support clinical and biomedical NER. We transfer learned information encoded in a multilingual-BERT model to a corpora of clinical narratives and biomedical-scientific papers in Brazilian Portuguese. To evaluate the performance of BioBERTpt, we ran NER experiments on two annotated corpora containing clinical narratives and compared the results with existing BERT models. Our in-domain model outperformed the baseline model in F1-score by 2.72%, achieving higher performance in 11 out of 13 assessed entities. We demonstrate that enriching contextual embedding models with domain literature can play an important role in improving performance for specific NLP tasks. The transfer learning process enhanced the Portuguese biomedical NER model by reducing the necessity of labeled data and the demand for retraining a whole new model.",38939829,https://www.aclweb.org/anthology/2020.clinicalnlp-1.7 +WS-12,WS-12.31,Cancer Registry Information Extraction via Transfer Learning,Yan-Jie Lin|Hong-Jie Dai|You-Chen Zhang|Chung-Yang Wu|Yu-Cheng Chang|Pin-Jou Lu|Chih-Jen Huang|Yu-Tsang Wang|Hui-Min Hsieh|Kun-San Chao|Tsang-Wu Liu|I-Shou Chang|Yi-Hsin Connie Yang|Ti-Hao Wang|Ko-Jiunn Liu|Li-Tzong Chen|Sheau-Fang Yang,"A cancer registry is a critical and massive database for which various types of domain knowledge are needed and whose maintenance requires labor-intensive data curation. In order to facilitate the curation process for building a high-quality and integrated cancer registry database, we compiled a cross-hospital corpus and applied neural network methods to develop a natural language processing system for extracting cancer registry variables buried in unstructured pathology reports. The performance of the developed networks was compared with various baselines using standard micro-precision, recall and F-measure. Furthermore, we conducted experiments to study the feasibility of applying transfer learning to rapidly develop a well-performing system for processing reports from different sources that might be presented in different writing styles and formats. The results demonstrate that the transfer learning method enables us to develop a satisfactory system for a new hospital with only a few annotations and suggest more opportunities to reduce the burden of cancer registry curation.",38939830,https://www.aclweb.org/anthology/2020.clinicalnlp-1.22 +WS-12,WS-12.33,Utilizing Multimodal Feature Consistency to Detect Adversarial Examples on Clinical Summaries,Wenjie Wang|Youngja Park|Taesung Lee|Ian Molloy|Pengfei Tang|Li Xiong,"Recent studies have shown that adversarial examples can be generated by applying small perturbations to the inputs such that the well- trained deep learning models will misclassify. With the increasing number of safety and security-sensitive applications of deep learn- ing models, the robustness of deep learning models has become a crucial topic. The robustness of deep learning models for health- care applications is especially critical because the unique characteristics and the high financial interests of the medical domain make it more sensitive to adversarial attacks. Among the modalities of medical data, the clinical summaries have higher risks to be attacked because they are generated by third-party companies. As few works studied adversarial threats on clinical summaries, in this work we first apply adversarial attack to clinical summaries of electronic health records (EHR) to show the text-based deep learning systems are vulnerable to adversarial examples. Secondly, benefiting from the multi-modality of the EHR dataset, we propose a novel defense method, MATCH (Multimodal feATure Consistency cHeck), which leverages the consistency between multiple modalities in the data to defend against adversarial examples on a single modality. Our experiments demonstrate the effectiveness of MATCH on a hospital readmission prediction task comparing with baseline methods.",38939831,https://www.aclweb.org/anthology/2020.clinicalnlp-1.29 +WS-12,WS-12.37,PHICON: Improving Generalization of Clinical Text De-identification Models via Data Augmentation,Xiang Yue|Shuang Zhou,"De-identification is the task of identifying protected health information (PHI) in the clinical text. Existing neural de-identification models often fail to generalize to a new dataset. We propose a simple yet effective data augmentation method PHICON to alleviate the generalization issue. PHICON consists of PHI augmentation and Context augmentation, which creates augmented training corpora by replacing PHI entities with named-entities sampled from external sources, and by changing background context with synonym replacement or random word insertion, respectively. Experimental results on the i2b2 2006 and 2014 de-identification challenge datasets show that PHICON can help three selected de-identification models boost F1-score (by at most 8.6%) on cross-dataset test setting. We also discuss how much augmentation to use and how each augmentation method influences the performance.",38939832,https://www.aclweb.org/anthology/2020.clinicalnlp-1.23 +WS-12,WS-12.38,Where's the Question? A Multi-channel Deep Convolutional Neural Network for Question Identification in Textual Data,George Michalopoulos|Helen Chen|Alexander Wong,"In most clinical practice settings, there is no rigorous reviewing of the clinical documentation, resulting in inaccurate information captured in the patient medical records. The gold standard in clinical data capturing is achieved via “expert-review”, where clinicians can have a dialogue with a domain expert (reviewers) and ask them questions about data entry rules. Automatically identifying “real questions” in these dialogues could uncover ambiguities or common problems in data capturing in a given clinical setting. In this study, we proposed a novel multi-channel deep convolutional neural network architecture, namely Quest-CNN, for the purpose of separating real questions that expect an answer (information or help) about an issue from sentences that are not questions, as well as from questions referring to an issue mentioned in a nearby sentence (e.g., can you clarify this?), which we will refer as “c-questions”. We conducted a comprehensive performance comparison analysis of the proposed multi-channel deep convolutional neural network against other deep neural networks. Furthermore, we evaluated the performance of traditional rule-based and learning-based methods for detecting question sentences. The proposed Quest-CNN achieved the best F1 score both on a dataset of data entry-review dialogue in a dialysis care setting, and on a general domain dataset.",38939833,https://www.aclweb.org/anthology/2020.clinicalnlp-1.24 +WS-12,WS-12.39,Advancing Seq2seq with Joint Paraphrase Learning,So Yeon Min|Preethi Raghavan|Peter Szolovits,"We address the problem of model generalization for sequence to sequence (seq2seq) architectures. We propose going beyond data augmentation via paraphrase-optimized multi-task learning and observe that it is useful in correctly handling unseen sentential paraphrases as inputs. Our models greatly outperform SOTA seq2seq models for semantic parsing on diverse domains (Overnight - up to 3.2% and emrQA - 7%) and Nematus, the winning solution for WMT 2017, for Czech to English translation (CzENG 1.6 - 1.5 BLEU).",38939834,https://www.aclweb.org/anthology/2020.clinicalnlp-1.30 +WS-12,WS-12.4,Comparison of Machine Learning Methods for Multi-label Classificationof Nursing Education and Licensure Exam Questions,John Langton|Krishna Srihasam|Junlin Jiang,"In this paper, we evaluate several machine learning methods for multi-label classification of text questions. Every nursing student in the United States must pass the National Council Licensure Examination (NCLEX) to begin professional practice. NCLEX defines a number of competencies on which students are evaluated. By labeling test questions with NCLEX competencies, we can score students according to their performance in each competency. This information helps instructors measure how prepared students are for the NCLEX, as well as which competencies they may need help with. A key challenge is that questions may be related to more than one competency. Labeling questions with NCLEX competencies, therefore, equates to a multi-label, text classification problem where each competency is a label. Here we present an evaluation of several methods to support this use case along with a proposed approach. While our work is grounded in the nursing education domain, the methods described here can be used for any multi-label, text classification use case.",38939809,https://www.aclweb.org/anthology/2020.clinicalnlp-1.10 +WS-12,WS-12.40,Learning from Unlabelled Data for Clinical Semantic Textual Similarity,Yuxia Wang|Karin Verspoor|Timothy Baldwin,"Domain pretraining followed by task fine-tuning has become the standard paradigm for NLP tasks, but requires in-domain labelled data for task fine-tuning. To overcome this, we propose to utilise domain unlabelled data by assigning pseudo labels from a general model. We evaluate the approach on two clinical STS datasets, and achieve r= 0.80 on N2C2-STS. Further investigation reveals that if the data distribution of unlabelled sentence pairs is closer to the test data, we can obtain better performance. By leveraging a large general-purpose STS dataset and small-scale in-domain training data, we obtain further improvements to r= 0.90, a new SOTA.",38939835,https://www.aclweb.org/anthology/2020.clinicalnlp-1.25 +WS-12,WS-12.43,BERT-XML: Large Scale Automated ICD Coding Using BERTPretraining,Zachariah Zhang|Jingshu Liu|Narges Razavian,"ICD coding is the task of classifying and cod-ing all diagnoses, symptoms and proceduresassociated with a patient’s visit. The process isoften manual, extremely time-consuming andexpensive for hospitals as clinical interactionsare usually recorded in free text medical notes.In this paper, we propose a machine learningmodel, BERT-XML, for large scale automatedICD coding of EHR notes, utilizing recentlydeveloped unsupervised pretraining that haveachieved state of the art performance on a va-riety of NLP tasks. We train a BERT modelfrom scratch on EHR notes, learning with vo-cabulary better suited for EHR tasks and thusoutperform off-the-shelf models. We furtheradapt the BERT architecture for ICD codingwith multi-label attention. We demonstratethe effectiveness of BERT-based models on thelarge scale ICD code classification task usingmillions of EHR notes to predict thousands ofunique codes.",38939836,https://www.aclweb.org/anthology/2020.clinicalnlp-1.3 +WS-12,WS-12.44,Joint Learning with Pre-trained Transformer on Named Entity Recognition and Relation Extraction Tasks for Clinical Analytics,Miao Chen|Ganhui Lan|Fang Du|Victor Lobanov,"In drug development, protocols define how clinical trials are conducted, and are therefore of paramount importance. They contain key patient-, investigator-, medication-, and study-related information, often elaborated in different sections in the protocol texts. Granular-level parsing on large quantity of existing protocols can accelerate clinical trial design and provide actionable insights into trial optimization. Here, we report our progresses in using deep learning NLP algorithms to enable automated protocol analytics. In particular, we combined a pre-trained BERT transformer model with joint-learning strategies to simultaneously identify clinically relevant entities (i.e. Named Entity Recognition) and extract the syntactic relations between these entities (i.e. Relation Extraction) from the eligibility criteria section in protocol texts. When comparing to standalone NER and RE models, our joint-learning strategy can effectively improve the performance of RE task while retaining similarly high NER performance, likely due to the synergy of optimizing toward both tasks’ objectives via shared parameters. The derived NLP model provides an end-to-end solution to convert unstructured protocol texts into structured data source, which will be embedded into a comprehensive clinical analytics workflow for downstream trial design missions such like patient population extraction, patient enrollment rate estimation, and protocol amendment prediction.",38939837,https://www.aclweb.org/anthology/2020.clinicalnlp-1.26 WS-12,WS-12.48,Analyzing Text Specific vs Blackbox Fairness Algorithms in Multimodal Clinical NLP,John Chen|Ian Berlot-Attwell|Xindi Wang|Safwan Hossain|Frank Rudzicz,"Clinical machine learning is increasingly multimodal, collected in both structured tabular formats and unstructured forms such as free text. We propose a novel task of exploring fairness on a multimodal clinical dataset, adopting equalized odds for the downstream medical prediction tasks. To this end, we investigate a modality-agnostic fairness algorithm - equalized odds post processing - and compare it to a text-specific fairness algorithm: debiased clinical word embeddings. Despite the fact that debiased word embeddings do not explicitly address equalized odds of protected groups, we show that a text-specific approach to fairness may simultaneously achieve a good balance of performance classical notions of fairness. Our work opens the door for future work at the critical intersection of clinical NLP and fairness.",38939838,https://www.aclweb.org/anthology/2020.clinicalnlp-1.33 -WS-12,WS-12.49,Extracting Semantic Aspects for Structured Representation of Clinical Trial Eligibility Criteria,Tirthankar Dasgupta|Ishani Mondal|Abir Naskar|Lipika Dey,,38939839,https://www.aclweb.org/anthology/2020.clinicalnlp-1.27 -WS-12,WS-12.6,Clinical XLNet: Modeling Sequential Clinical Notes and Predicting Prolonged Mechanical Ventilation,Kexin Huang|Abhishek Singh|Sitong Chen|Edward Moseley|Chih-Ying Deng|Naomi George|Charolotta Lindvall,,38939810,https://www.aclweb.org/anthology/2020.clinicalnlp-1.11 -WS-12,WS-12.7,Automatic recognition of abdominal lymph nodes from clinical text,Yifan Peng|Sungwon Lee|Daniel C. Elton|Thomas Shen|Yu-xing Tang|Qingyu Chen|Shuai Wang|Yingying Zhu|Ronald Summers|Zhiyong Lu,,38939811,https://www.aclweb.org/anthology/2020.clinicalnlp-1.12 -WS-12,WS-12.8,On the diminishing return of labeling clinical reports,Jean-Baptiste Lamare|Oloruntobiloba Olatunji|Li Yao,,38939812,https://www.aclweb.org/anthology/2020.clinicalnlp-1.31 -WS-13,WS-13.1,Correcting the Misuse: A Method for the Chinese Idiom Cloze Test,Xinyu Wang|Hongsheng Zhao|Tan Yang|Hongbo Wang,,38939724,https://www.aclweb.org/anthology/2020.deelio-1.1 -WS-13,WS-13.1008,AutoETER: Automated Entity Type Representation for Knowledge Graph Embedding,Yongfei Zhang,,38940167,https://www.aclweb.org/anthology/2020.findings-emnlp.105 -WS-13,WS-13.1039,Dynamic Semantic Matching and Aggregation Network for Few-shot Intent Detection,Hoang Nguyen,,38940168,https://www.aclweb.org/anthology/2020.findings-emnlp.108 +WS-12,WS-12.49,Extracting Semantic Aspects for Structured Representation of Clinical Trial Eligibility Criteria,Tirthankar Dasgupta|Ishani Mondal|Abir Naskar|Lipika Dey,"Eligibility criteria in the clinical trials specify the characteristics that a patient must or must not possess in order to be treated according to a standard clinical care guideline. As the process of manual eligibility determination is time-consuming, automatic structuring of the eligibility criteria into various semantic categories or aspects is the need of the hour. Existing methods use hand-crafted rules and feature-based statistical machine learning methods to dynamically induce semantic aspects. However, in order to deal with paucity of aspect-annotated clinical trials data, we propose a novel weakly-supervised co-training based method which can exploit a large pool of unlabeled criteria sentences to augment the limited supervised training data, and consequently enhance the performance. Experiments with 0.2M criteria sentences show that the proposed approach outperforms the competitive supervised baselines by 12% in terms of micro-averaged F1 score for all the aspects. Probing deeper into analysis, we observe domain-specific information boosts up the performance by a significant margin.",38939839,https://www.aclweb.org/anthology/2020.clinicalnlp-1.27 +WS-12,WS-12.6,Clinical XLNet: Modeling Sequential Clinical Notes and Predicting Prolonged Mechanical Ventilation,Kexin Huang|Abhishek Singh|Sitong Chen|Edward Moseley|Chih-Ying Deng|Naomi George|Charolotta Lindvall,"Clinical notes contain rich information, which is relatively unexploited in predictive modeling compared to structured data. In this work, we developed a new clinical text representation Clinical XLNet that leverages the temporal information of the sequence of the notes. We evaluated our models on prolonged mechanical ventilation prediction problem and our experiments demonstrated that Clinical XLNet outperforms the best baselines consistently. The models and scripts are made publicly available.",38939810,https://www.aclweb.org/anthology/2020.clinicalnlp-1.11 +WS-12,WS-12.7,Automatic recognition of abdominal lymph nodes from clinical text,Yifan Peng|Sungwon Lee|Daniel C. Elton|Thomas Shen|Yu-xing Tang|Qingyu Chen|Shuai Wang|Yingying Zhu|Ronald Summers|Zhiyong Lu,"Lymph node status plays a pivotal role in the treatment of cancer. The extraction of lymph nodes from radiology text reports enables large-scale training of lymph node detection on MRI. In this work, we first propose an ontology of 41 types of abdominal lymph nodes with a hierarchical relationship. We then introduce an end-to-end approach based on the combination of rules and transformer-based methods to detect these abdominal lymph node mentions and classify their types from the MRI radiology reports. We demonstrate the superior performance of a model fine-tuned on MRI reports using BlueBERT, called MriBERT. We find that MriBERT outperforms the rule-based labeler (0.957 vs 0.644 in micro weighted F1-score) as well as other BERT-based variations (0.913 - 0.928). We make the code and MriBERT publicly available at https://github.com/ncbi-nlp/bluebert, with the hope that this method can facilitate the development of medical report annotators to produce labels from scratch at scale.",38939811,https://www.aclweb.org/anthology/2020.clinicalnlp-1.12 +WS-12,WS-12.8,On the diminishing return of labeling clinical reports,Jean-Baptiste Lamare|Oloruntobiloba Olatunji|Li Yao,"Ample evidence suggests that better machine learning models may be steadily obtained by training on increasingly larger datasets on natural language processing (NLP) problems from non-medical domains. Whether the same holds true for medical NLP has by far not been thoroughly investigated. This work shows that this is indeed not always the case. We reveal the somehow counter-intuitive observation that performant medical NLP models may be obtained with small amount of labeled data, quite the opposite to the common belief, most likely due to the domain specificity of the problem. We show quantitatively the effect of training data size on a fixed test set composed of two of the largest public chest x-ray radiology report datasets on the task of abnormality classification. The trained models not only make use of the training data efficiently, but also outperform the current state-of-the-art rule-based systems by a significant margin.",38939812,https://www.aclweb.org/anthology/2020.clinicalnlp-1.31 +WS-13,WS-13.1,Correcting the Misuse: A Method for the Chinese Idiom Cloze Test,Xinyu Wang|Hongsheng Zhao|Tan Yang|Hongbo Wang,"The cloze test for Chinese idioms is a new challenge in machine reading comprehension: given a sentence with a blank, choosing a candidate Chinese idiom which matches the context. Chinese idiom is a type of Chinese idiomatic expression. The common misuse of Chinese idioms leads to error in corpus and causes error in the learned semantic representation of Chinese idioms. In this paper, we introduce the definition written by Chinese experts to correct the misuse. We propose a model for the Chinese idiom cloze test integrating various information effectively. We propose an attention mechanism called Attribute Attention to balance the weight of different attributes among different descriptions of the Chinese idiom. Besides the given candidates of every blank, we also try to choose the answer from all Chinese idioms that appear in the dataset as the extra loss due to the uniqueness and specificity of Chinese idioms. In experiments, our model outperforms the state-of-the-art model.",38939724,https://www.aclweb.org/anthology/2020.deelio-1.1 +WS-13,WS-13.1008,AutoETER: Automated Entity Type Representation for Knowledge Graph Embedding,Yongfei Zhang,"Recent advances in Knowledge Graph Embedding (KGE) allow for representing entities and relations in continuous vector spaces. Some traditional KGE models leveraging additional type information can improve the representation of entities which however totally rely on the explicit types or neglect the diverse type representations specific to various relations. Besides, none of the existing methods is capable of inferring all the relation patterns of symmetry, inversion and composition as well as the complex properties of 1-N, N-1 and N-N relations, simultaneously. To explore the type information for any KG, we develop a novel KGE framework with Automated Entity TypE Representation (AutoETER), which learns the latent type embedding of each entity by regarding each relation as a translation operation between the types of two entities with a relation-aware projection mechanism. Particularly, our designed automated type representation learning mechanism is a pluggable module which can be easily incorporated with any KGE model. Besides, our approach could model and infer all the relation patterns and complex relations. Experiments on four datasets demonstrate the superior performance of our model compared to state-of-the-art baselines on link prediction tasks, and the visualization of type clustering provides clearly the explanation of type embeddings and verifies the effectiveness of our model.",38940167,https://www.aclweb.org/anthology/2020.findings-emnlp.105 +WS-13,WS-13.1039,Dynamic Semantic Matching and Aggregation Network for Few-shot Intent Detection,Hoang Nguyen,"Few-shot Intent Detection is challenging due to the scarcity of available annotated utterances. Although recent works demonstrate that multi-level matching plays an important role in transferring learned knowledge from seen training classes to novel testing classes, they rely on a static similarity measure and overly fine-grained matching components. These limitations inhibit generalizing capability towards Generalized Few-shot Learning settings where both seen and novel classes are co-existent. In this paper, we propose a novel Semantic Matching and Aggregation Network where semantic components are distilled from utterances via multi-head self-attention with additional dynamic regularization constraints. These semantic components capture high-level information, resulting in more effective matching between instances. Our multi-perspective matching method provides a comprehensive matching measure to enhance representations of both labeled and unlabeled instances. We also propose a more challenging evaluation setting that considers classification on the joint all-class label space. Extensive experimental results demonstrate the effectiveness of our method. Our code and data are publicly available.",38940168,https://www.aclweb.org/anthology/2020.findings-emnlp.108 WS-13,WS-13.1059,Biomedical Event Extraction on Graph Edge-conditioned Attention Networks with Hierarchical Knowledge Graphs,Kung-Hsiang Huang,,38940169, -WS-13,WS-13.12,Entity Attribute Relation Extraction with Attribute-Aware Embeddings,Dan Iter|Xiao Yu|Fangtao Li,,38939729,https://www.aclweb.org/anthology/2020.deelio-1.6 -WS-13,WS-13.1524,Continual Learning Long Short Term Memory,Xiaolong Wang,,38940170,https://www.aclweb.org/anthology/2020.findings-emnlp.164 -WS-13,WS-13.16,Enhancing Question Answering by Injecting Ontological Knowledgethrough Regularization,Travis Goodwin|Dina Demner-Fushman,,38939730,https://www.aclweb.org/anthology/2020.deelio-1.7 -WS-13,WS-13.17,Target Concept Guided Medical Concept Normalization in Noisy User-Generated Texts,Katikapalli Subramanyam Kalyan|Sivanesan Sangeetha,,38939731,https://www.aclweb.org/anthology/2020.deelio-1.8 -WS-13,WS-13.18,Incorporating Commonsense Knowledge Graph in Pretrained Models for Social Commonsense Tasks,Ting-Yun Chang|Yang Liu|Karthik Gopalakrishnan|Behnam Hedayatnia|Pei Zhou|Dilek Hakkani-Tur,,38939732,https://www.aclweb.org/anthology/2020.deelio-1.9 -WS-13,WS-13.20,Commonsense Statements Identification and Explanation with Transformer based Encoders,Sonia Cibu|Anca Marginean,,38939733,https://www.aclweb.org/anthology/2020.deelio-1.10 -WS-13,WS-13.2047,Out-of-Sample Representation Learning for Knowledge Graphs,Marjan Albooyeh,,38940171,https://www.aclweb.org/anthology/2020.findings-emnlp.241 +WS-13,WS-13.12,Entity Attribute Relation Extraction with Attribute-Aware Embeddings,Dan Iter|Xiao Yu|Fangtao Li,"Entity-attribute relations are a fundamental component for building large-scale knowledge bases, which are widely employed in modern search engines. However, most such knowledge bases are manually curated, covering only a small fraction of all attributes, even for common entities. To improve the precision of model-based entity-attribute extraction, we propose attribute-aware embeddings, which embeds entities and attributes in the same space by the similarity of their attributes. Our model, EANET, learns these embeddings by representing entities as a weighted sum of their attributes and concatenates these embeddings to mention level features. EANET achieves up to 91% classification accuracy, outperforming strong baselines and achieves 83% precision on manually labeled high confidence extractions, outperforming Biperpedia (Gupta et al., 2014), a previous state-of-the-art for large scale entity-attribute extraction.",38939729,https://www.aclweb.org/anthology/2020.deelio-1.6 +WS-13,WS-13.1524,Continual Learning Long Short Term Memory,Xiaolong Wang,"Catastrophic forgetting in neural networks indicates the performance decreasing of deep learning models on previous tasks while learning new tasks. To address this problem, we propose a novel Continual Learning Long Short Term Memory (CL-LSTM) cell in Recurrent Neural Network (RNN) in this paper. CL-LSTM considers not only the state of each individual task’s output gates but also the correlation of the states between tasks, so that the deep learning models can incrementally learn new tasks without catastrophically forgetting previously tasks. Experimental results demonstrate significant improvements of CL-LSTM over state-of-the-art approaches on spoken language understanding (SLU) tasks.",38940170,https://www.aclweb.org/anthology/2020.findings-emnlp.164 +WS-13,WS-13.16,Enhancing Question Answering by Injecting Ontological Knowledgethrough Regularization,Travis Goodwin|Dina Demner-Fushman,"Deep neural networks have demonstrated high performance on many natural language processing (NLP) tasks that can be answered directly from text, and have struggled to solve NLP tasks requiring external (e.g., world) knowledge. In this paper, we present OSCR (Ontology-based Semantic Composition Regularization), a method for injecting task-agnostic knowledge from an Ontology or knowledge graph into a neural network during pre-training. We evaluated the performance of BERT pre-trained on Wikipedia with and without OSCR by measuring the performance when fine-tuning on two question answering tasks involving world knowledge and causal reasoning and one requiring domain (healthcare) knowledge and obtained 33.3%, 18.6%, and 4% improved accuracy compared to pre-training BERT without OSCR.",38939730,https://www.aclweb.org/anthology/2020.deelio-1.7 +WS-13,WS-13.17,Target Concept Guided Medical Concept Normalization in Noisy User-Generated Texts,Katikapalli Subramanyam Kalyan|Sivanesan Sangeetha,"Medical concept normalization (MCN) i.e., mapping of colloquial medical phrases to standard concepts is an essential step in analysis of medical social media text. The main drawback in existing state-of-the-art approach (Kalyan and Sangeetha, 2020b) is learning target concept vector representations from scratch which requires more number of training instances. Our model is based on RoBERTa and target concept embeddings. In our model, we integrate a) target concept information in the form of target concept vectors generated by encoding target concept descriptions using SRoBERTa, state-of-the-art RoBERTa based sentence embedding model and b) domain lexicon knowledge by enriching target concept vectors with synonym relationship knowledge using retrofitting algorithm. It is the first attempt in MCN to exploit both target concept information as well as domain lexicon knowledge in the form of retrofitted target concept vectors. Our model outperforms all the existing models with an accuracy improvement up to 1.36% on three standard datasets. Further, our model when trained only on mapping lexicon synonyms achieves up to 4.87% improvement in accuracy.",38939731,https://www.aclweb.org/anthology/2020.deelio-1.8 +WS-13,WS-13.18,Incorporating Commonsense Knowledge Graph in Pretrained Models for Social Commonsense Tasks,Ting-Yun Chang|Yang Liu|Karthik Gopalakrishnan|Behnam Hedayatnia|Pei Zhou|Dilek Hakkani-Tur,"Pretrained language models have excelled at many NLP tasks recently; however, their social intelligence is still unsatisfactory. To enable this, machines need to have a more general understanding of our complicated world and develop the ability to perform commonsense reasoning besides fitting the specific downstream tasks. External commonsense knowledge graphs (KGs), such as ConceptNet, provide rich information about words and their relationships. Thus, towards general commonsense learning, we propose two approaches to implicitly and explicitly infuse such KGs into pretrained language models. We demonstrate our proposed methods perform well on SocialIQA, a social commonsense reasoning task, in both limited and full training data regimes.",38939732,https://www.aclweb.org/anthology/2020.deelio-1.9 +WS-13,WS-13.20,Commonsense Statements Identification and Explanation with Transformer based Encoders,Sonia Cibu|Anca Marginean,"In this work, we present our empirical attempt to identify the proper strategy of using Transformer Language Models to identify sentences consistent with commonsense. We tackle the first two tasks from the ComVE competition. The starting point for our work is the BERT assumption according to which a large number of NLP tasks can be solved with pre-trained Transformers with no substantial task-specific changes of the architecture. However, our experiments show that the encoding strategy can have a great impact on the quality of the fine-tuning. The combination between cross-encoding and multi-input models worked better than one cross-encoder and allowed us to achieve comparable results with the state-of-the-art without the use of any external data.",38939733,https://www.aclweb.org/anthology/2020.deelio-1.10 +WS-13,WS-13.2047,Out-of-Sample Representation Learning for Knowledge Graphs,Marjan Albooyeh,"Many important problems can be formulated as reasoning in knowledge graphs. Representation learning has proved extremely effective for transductive reasoning, in which one needs to make new predictions for already observed entities. This is true for both attributed graphs(where each entity has an initial feature vector) and non-attributed graphs (where the only initial information derives from known relations with other entities). For out-of-sample reasoning, where one needs to make predictions for entities that were unseen at training time, much prior work considers attributed graph. However, this problem is surprisingly under-explored for non-attributed graphs. In this paper, we study the out-of-sample representation learning problem for non-attributed knowledge graphs, create benchmark datasets for this task, develop several models and baselines, and provide empirical analyses and comparisons of the proposed models and baselines.",38940171,https://www.aclweb.org/anthology/2020.findings-emnlp.241 WS-13,WS-13.2085,Visual Objects As Context: Exploiting Visual Objects for Lexical Entailment,Masayasu Muraoka,"We propose a new word representation method derived from visual objects in associated images to tackle the lexical entailment task. Although it has been shown that the Distributional Informativeness Hypothesis (DIH) holds on text, in which the DIH assumes that a context surrounding a hyponym is more informative than that of a hypernym, it has never been tested on visual objects. Since our perception is tightly associated with language, it is meaningful to explore whether the DIH holds on visual objects. To this end, we consider visual objects as the context of a word and represent a word as a bag of visual objects found in images associated with the word. This allows us to test the feasibility of the visual DIH. To better distinguish word pairs in a hypernym relation from other relations such as co-hypernyms, we also propose a new measurable function that takes into account both the difference in the generality of meaning and similarity of meaning between words. Our experimental results show that the DIH holds on visual objects and that the proposed method combined with the proposed function outperforms existing unsupervised representation methods.",38940172,https://www.aclweb.org/anthology/2020.findings-emnlp.246 -WS-13,WS-13.22,"On the Complementary Nature of Knowledge Graph Embedding, Fine Grain Entity Types, and Language Modeling",Rajat Patel|Francis Ferraro,,38939734,https://www.aclweb.org/anthology/2020.deelio-1.11 -WS-13,WS-13.2513,BERT-kNN: Adding a kNN Search Component to Pretrained Language Models for Better QA,Nora Kassner,,38940173,https://www.aclweb.org/anthology/2020.findings-emnlp.307 -WS-13,WS-13.2666,Cross-Lingual Text Classification with Minimal Resources by Transferring a Sparse Teacher,Giannis Karamanolakis,,38940174,https://www.aclweb.org/anthology/2020.findings-emnlp.323 -WS-13,WS-13.4,Relation Extraction with Contextualized Relation Embedding,Xiaoyu Chen|Rohan Badlani,,38939725,https://www.aclweb.org/anthology/2020.deelio-1.2 -WS-13,WS-13.6,Generalization to Mitigate Synonym Substitution Attacks,Basemah Alshemali|Jugal Kalita,,38939726,https://www.aclweb.org/anthology/2020.deelio-1.3 -WS-13,WS-13.696,E-BERT: Efficient-Yet-Effective Entity Embeddings for BERT,Nina Poerner,,38940166,https://www.aclweb.org/anthology/2020.findings-emnlp.71 -WS-13,WS-13.7,Data Augmentation for Finetuning Text Generators,Steven Y. Feng|Varun Gangal|Dongyeop Kang|Teruko Mitamura|Eduard Hovy,,38939727,https://www.aclweb.org/anthology/2020.deelio-1.4 -WS-13,WS-13.9,Common Sense or World Knowledge? Investigating Adapter-Based Knowledge Injection into Pretrained Transformers,Anne Lauscher|Olga Majewska|Leonardo F. R. Ribeiro|Iryna Gurevych|Nikolai Rozanov|Goran Glavaš,,38939728,https://www.aclweb.org/anthology/2020.deelio-1.5 -WS-15,WS-15.1,Knowing Right from Wrong: Should We Use More Complex Models for Automatic Short-Answer Scoring in Bahasa Indonesia?,Ali Akbar Septiandri|Yosef Ardhito Winatmoko|Ilham Firdausi Putra,,38939419,https://www.aclweb.org/anthology/2020.sustainlp-1.1 -WS-15,WS-15.1098,OptSLA: an Optimization-Based Approach for Sequential Label Aggregation,Qi Li,,38940107,https://www.aclweb.org/anthology/2020.findings-emnlp.119 -WS-15,WS-15.12,Exploring the Boundaries of Low-Resource BERT Distillation,Moshe Wasserblat|Oren Pereg|Peter Izsak,,38939426,https://www.aclweb.org/anthology/2020.sustainlp-1.5 -WS-15,WS-15.1286,Inexpensive Domain Adaptation of Pretrained Language Models: Case Studies on Biomedical NER and Covid-19 QA,Nina Poerner,,38940121,https://www.aclweb.org/anthology/2020.findings-emnlp.134 -WS-15,WS-15.13,Efficient Estimation of Influence of a Training Instance,Sosuke Kobayashi|Sho Yokoi|Jun Suzuki|Kentaro Inui,,38939427,https://www.aclweb.org/anthology/2020.sustainlp-1.6 -WS-15,WS-15.14,Efficient Inference For Neural Machine Translation,Yi-Te Hsu|Sarthak Garg|Yi-Hsiu Liao|Ilya Chatsviorkin,,38939429,https://www.aclweb.org/anthology/2020.sustainlp-1.7 -WS-15,WS-15.1418,TopicBERT for Energy Efficient Document Classification,Yatin Chaudhary,,38940122,https://www.aclweb.org/anthology/2020.findings-emnlp.152 -WS-15,WS-15.1537,Constrained Decoding for Computationally Efficient Named Entity Recognition Taggers,Brian Lester,,38940105,https://www.aclweb.org/anthology/2020.findings-emnlp.166 -WS-15,WS-15.17,Sparse Optimization for Unsupervised Extractive Summarization of Long Documents with the Frank-Wolfe Algorithm,Alicia Tsai|Laurent El Ghaoui,,38939430,https://www.aclweb.org/anthology/2020.sustainlp-1.8 +WS-13,WS-13.22,"On the Complementary Nature of Knowledge Graph Embedding, Fine Grain Entity Types, and Language Modeling",Rajat Patel|Francis Ferraro,"We demonstrate the complementary natures of neural knowledge graph embedding, fine-grain entity type prediction, and neural language modeling. We show that a language model-inspired knowledge graph embedding approach yields both improved knowledge graph embeddings and fine-grain entity type representations. Our work also shows that jointly modeling both structured knowledge tuples and language improves both.",38939734,https://www.aclweb.org/anthology/2020.deelio-1.11 +WS-13,WS-13.2513,BERT-kNN: Adding a kNN Search Component to Pretrained Language Models for Better QA,Nora Kassner,"Khandelwal et al. (2020) use a k-nearest-neighbor (kNN) component to improve language model performance. We show that this idea is beneficial for open-domain question answering (QA). To improve the recall of facts encountered during training, we combine BERT (Devlin et al., 2019) with a traditional information retrieval step (IR) and a kNN search over a large datastore of an embedded text collection. Our contributions are as follows: i) BERT-kNN outperforms BERT on cloze-style QA by large margins without any further training. ii) We show that BERT often identifies the correct response category (e.g., US city), but only kNN recovers the factually correct answer (e.g.,“Miami”). iii) Compared to BERT, BERT-kNN excels for rare facts. iv) BERT-kNN can easily handle facts not covered by BERT’s training set, e.g., recent events.",38940173,https://www.aclweb.org/anthology/2020.findings-emnlp.307 +WS-13,WS-13.2666,Cross-Lingual Text Classification with Minimal Resources by Transferring a Sparse Teacher,Giannis Karamanolakis,"Cross-lingual text classification alleviates the need for manually labeled documents in a target language by leveraging labeled documents from other languages. Existing approaches for transferring supervision across languages require expensive cross-lingual resources, such as parallel corpora, while less expensive cross-lingual representation learning approaches train classifiers without target labeled documents. In this work, we propose a cross-lingual teacher-student method, CLTS, that generates “weak” supervision in the target language using minimal cross-lingual resources, in the form of a small number of word translations. Given a limited translation budget, CLTS extracts and transfers only the most important task-specific seed words across languages and initializes a teacher classifier based on the translated seed words. Then, CLTS iteratively trains a more powerful student that also exploits the context of the seed words in unlabeled target documents and outperforms the teacher. CLTS is simple and surprisingly effective in 18 diverse languages: by transferring just 20 seed words, even a bag-of-words logistic regression student outperforms state-of-the-art cross-lingual methods (e.g., based on multilingual BERT). Moreover, CLTS can accommodate any type of student classifier: leveraging a monolingual BERT student leads to further improvements and outperforms even more expensive approaches by up to 12% in accuracy. Finally, CLTS addresses emerging tasks in low-resource languages using just a small number of word translations.",38940174,https://www.aclweb.org/anthology/2020.findings-emnlp.323 +WS-13,WS-13.4,Relation Extraction with Contextualized Relation Embedding,Xiaoyu Chen|Rohan Badlani,This submission is a paper that proposes an architecture for the relation extraction task which integrates semantic information with knowledge base modeling in a novel manner.,38939725,https://www.aclweb.org/anthology/2020.deelio-1.2 +WS-13,WS-13.6,Generalization to Mitigate Synonym Substitution Attacks,Basemah Alshemali|Jugal Kalita,"Studies have shown that deep neural networks (DNNs) are vulnerable to adversarial examples – perturbed inputs that cause DNN-based models to produce incorrect results. One robust adversarial attack in the NLP domain is the synonym substitution. In attacks of this variety, the adversary substitutes words with synonyms. Since synonym substitution perturbations aim to satisfy all lexical, grammatical, and semantic constraints, they are difficult to detect with automatic syntax check as well as by humans. In this paper, we propose a structure-free defensive method that is capable of improving the performance of DNN-based models with both clean and adversarial data. Our findings show that replacing the embeddings of the important words in the input samples with the average of their synonyms’ embeddings can significantly improve the generalization of DNN-based classifiers. By doing so, we reduce model sensitivity to particular words in the input samples. Our results indicate that the proposed defense is not only capable of defending against adversarial attacks, but is also capable of improving the performance of DNN-based models when tested on benign data. On average, the proposed defense improved the classification accuracy of the CNN and Bi-LSTM models by 41.30% and 55.66%, respectively, when tested under adversarial attacks. Extended investigation shows that our defensive method can improve the robustness of nonneural models, achieving an average of 17.62% and 22.93% classification accuracy increase on the SVM and XGBoost models, respectively. The proposed defensive method has also shown an average of 26.60% classification accuracy improvement when tested with the infamous BERT model. Our algorithm is generic enough to be applied in any NLP domain and to any model trained on any natural language.",38939726,https://www.aclweb.org/anthology/2020.deelio-1.3 +WS-13,WS-13.696,E-BERT: Efficient-Yet-Effective Entity Embeddings for BERT,Nina Poerner,"We present a novel way of injecting factual knowledge about entities into the pretrained BERT model (Devlin et al., 2019): We align Wikipedia2Vec entity vectors (Yamada et al., 2016) with BERT’s native wordpiece vector space and use the aligned entity vectors as if they were wordpiece vectors. The resulting entity-enhanced version of BERT (called E-BERT) is similar in spirit to ERNIE (Zhang et al., 2019) and KnowBert (Peters et al., 2019), but it requires no expensive further pre-training of the BERT encoder. We evaluate E-BERT on unsupervised question answering (QA), supervised relation classification (RC) and entity linking (EL). On all three tasks, E-BERT outperforms BERT and other baselines. We also show quantitatively that the original BERT model is overly reliant on the surface form of entity names (e.g., guessing that someone with an Italian-sounding name speaks Italian), and that E-BERT mitigates this problem.",38940166,https://www.aclweb.org/anthology/2020.findings-emnlp.71 +WS-13,WS-13.7,Data Augmentation for Finetuning Text Generators,Steven Y. Feng|Varun Gangal|Dongyeop Kang|Teruko Mitamura|Eduard Hovy,"In this paper, we investigate data augmentation for text generation, which we call GenAug. Text generation and language modeling are important tasks within natural language processing, and are especially challenging for low-data regimes. We propose and evaluate various augmentation methods, including some that incorporate external knowledge, for finetuning GPT-2 on a subset of Yelp Reviews. We also examine the relationship between the amount of augmentation and the quality of the generated text. We utilize several metrics that evaluate important aspects of the generated text including its diversity and fluency. Our experiments demonstrate that insertion of character-level synthetic noise and keyword replacement with hypernyms are effective augmentation methods, and that the quality of generations improves to a peak at approximately three times the amount of original data.",38939727,https://www.aclweb.org/anthology/2020.deelio-1.4 +WS-13,WS-13.9,Common Sense or World Knowledge? Investigating Adapter-Based Knowledge Injection into Pretrained Transformers,Anne Lauscher|Olga Majewska|Leonardo F. R. Ribeiro|Iryna Gurevych|Nikolai Rozanov|Goran Glavaš,"Following the major success of neural language models (LMs) such as BERT or GPT-2 on a variety of language understanding tasks, recent work focused on injecting (structured) knowledge from external resources into these models. While on the one hand, joint pre-training (i.e., training from scratch, adding objectives based on external knowledge to the primary LM objective) may be prohibitively computationally expensive, post-hoc fine-tuning on external knowledge, on the other hand, may lead to the catastrophic forgetting of distributional knowledge. In this work, we investigate models for complementing the distributional knowledge of BERT with conceptual knowledge from ConceptNet and its corresponding Open Mind Common Sense (OMCS) corpus, respectively, using adapter training. While overall results on the GLUE benchmark paint an inconclusive picture, a deeper analysis reveals that our adapter-based models substantially outperform BERT (up to 15-20 performance points) on inference tasks that require the type of conceptual knowledge explicitly present in ConceptNet and OMCS. We also open source all our experiments and relevant code under: https://github.com/wluper/retrograph.",38939728,https://www.aclweb.org/anthology/2020.deelio-1.5 +WS-15,WS-15.1,Knowing Right from Wrong: Should We Use More Complex Models for Automatic Short-Answer Scoring in Bahasa Indonesia?,Ali Akbar Septiandri|Yosef Ardhito Winatmoko|Ilham Firdausi Putra,"We compare three solutions to UKARA 1.0 challenge on automated short-answer scoring: single classical, ensemble classical, and deep learning. The task is to classify given answers to two questions, whether they are right or wrong. While recent development shows increasing model complexity to push the benchmark performances, they tend to be resource-demanding with mundane improvement. For the UKARA task, we found that bag-of-words and classical machine learning approaches can compete with ensemble models and Bi-LSTM model with pre-trained word2vec embedding from 200 million words. In this case, the single classical machine learning achieved less than 2% difference in F1 compared to the deep learning approach with 1/18 time for model training.",38939419,https://www.aclweb.org/anthology/2020.sustainlp-1.1 +WS-15,WS-15.1098,OptSLA: an Optimization-Based Approach for Sequential Label Aggregation,Qi Li,"The need for the annotated training dataset on which data-hungry machine learning algorithms feed has increased dramatically with advanced acclaim of machine learning applications. To annotate the data, people with domain expertise are needed, but they are seldom available and expensive to hire. This has lead to the thriving of crowdsourcing platforms such as Amazon Mechanical Turk (AMT). However, the annotations provided by one worker cannot be used directly to train the model due to the lack of expertise. Existing literature in annotation aggregation focuses on binary and multi-choice problems. In contrast, little work has been done on complex tasks such as sequence labeling with imbalanced classes, a ubiquitous task in Natural Language Processing (NLP), and Bio-Informatics. We propose OptSLA, an Optimization-based Sequential Label Aggregation method, that jointly considers the characteristics of sequential labeling tasks, workers reliabilities, and advanced deep learning techniques to conquer the challenge. We evaluate our model on crowdsourced data for named entity recognition task. Our results show that the proposed OptSLA outperforms the state-of-the-art aggregation methods, and the results are easier to interpret.",38940107,https://www.aclweb.org/anthology/2020.findings-emnlp.119 +WS-15,WS-15.12,Exploring the Boundaries of Low-Resource BERT Distillation,Moshe Wasserblat|Oren Pereg|Peter Izsak,"In recent years, large pre-trained models have demonstrated state-of-the-art performance in many of NLP tasks. However, the deployment of these models on devices with limited resources is challenging due to the models’ large computational consumption and memory requirements. Moreover, the need for a considerable amount of labeled training data also hinders real-world deployment scenarios. Model distillation has shown promising results for reducing model size, computational load and data efficiency. In this paper we test the boundaries of BERT model distillation in terms of model compression, inference efficiency and data scarcity. We show that classification tasks that require the capturing of general lexical semantics can be successfully distilled by very simple and efficient models and require relatively small amount of labeled training data. We also show that the distillation of large pre-trained models is more effective in real-life scenarios where limited amounts of labeled training are available.",38939426,https://www.aclweb.org/anthology/2020.sustainlp-1.5 +WS-15,WS-15.1286,Inexpensive Domain Adaptation of Pretrained Language Models: Case Studies on Biomedical NER and Covid-19 QA,Nina Poerner,"Domain adaptation of Pretrained Language Models (PTLMs) is typically achieved by unsupervised pretraining on target-domain text. While successful, this approach is expensive in terms of hardware, runtime and CO 2 emissions. Here, we propose a cheaper alternative: We train Word2Vec on target-domain text and align the resulting word vectors with the wordpiece vectors of a general-domain PTLM. We evaluate on eight English biomedical Named Entity Recognition (NER) tasks and compare against the recently proposed BioBERT model. We cover over 60% of the BioBERT - BERT F1 delta, at 5% of BioBERT’s CO 2 footprint and 2% of its cloud compute cost. We also show how to quickly adapt an existing general-domain Question Answering (QA) model to an emerging domain: the Covid-19 pandemic.",38940121,https://www.aclweb.org/anthology/2020.findings-emnlp.134 +WS-15,WS-15.13,Efficient Estimation of Influence of a Training Instance,Sosuke Kobayashi|Sho Yokoi|Jun Suzuki|Kentaro Inui,"Understanding the influence of a training instance on a neural network model leads to improving interpretability. However, it is difficult and inefficient to evaluate the influence, which shows how a model’s prediction would be changed if a training instance were not used. In this paper, we propose an efficient method for estimating the influence. Our method is inspired by dropout, which zero-masks a sub-network and prevents the sub-network from learning each training instance. By switching between dropout masks, we can use sub-networks that learned or did not learn each training instance and estimate its influence. Through experiments with BERT and VGGNet on classification datasets, we demonstrate that the proposed method can capture training influences, enhance the interpretability of error predictions, and cleanse the training dataset for improving generalization.",38939427,https://www.aclweb.org/anthology/2020.sustainlp-1.6 +WS-15,WS-15.14,Efficient Inference For Neural Machine Translation,Yi-Te Hsu|Sarthak Garg|Yi-Hsiu Liao|Ilya Chatsviorkin,"Large Transformer models have achieved state-of-the-art results in neural machine translation and have become standard in the field. In this work, we look for the optimal combination of known techniques to optimize inference speed without sacrificing translation quality. We conduct an empirical study that stacks various approaches and demonstrates that combination of replacing decoder self-attention with simplified recurrent units, adopting a deep encoder and a shallow decoder architecture and multi-head attention pruning can achieve up to 109% and 84% speedup on CPU and GPU respectively and reduce the number of parameters by 25% while maintaining the same translation quality in terms of BLEU.",38939429,https://www.aclweb.org/anthology/2020.sustainlp-1.7 +WS-15,WS-15.1418,TopicBERT for Energy Efficient Document Classification,Yatin Chaudhary,"Prior research notes that BERT’s computational cost grows quadratically with sequence length thus leading to longer training times, higher GPU memory constraints and carbon emissions. While recent work seeks to address these scalability issues at pre-training, these issues are also prominent in fine-tuning especially for long sequence tasks like document classification. Our work thus focuses on optimizing the computational cost of fine-tuning for document classification. We achieve this by complementary learning of both topic and language models in a unified framework, named TopicBERT. This significantly reduces the number of self-attention operations – a main performance bottleneck. Consequently, our model achieves a 1.4x ( 40%) speedup with 40% reduction in CO2 emission while retaining 99.9% performance over 5 datasets.",38940122,https://www.aclweb.org/anthology/2020.findings-emnlp.152 +WS-15,WS-15.1537,Constrained Decoding for Computationally Efficient Named Entity Recognition Taggers,Brian Lester,"Current state-of-the-art models for named entity recognition (NER) are neural models with a conditional random field (CRF) as the final layer. Entities are represented as per-token labels with a special structure in order to decode them into spans. Current work eschews prior knowledge of how the span encoding scheme works and relies on the CRF learning which transitions are illegal and which are not to facilitate global coherence. We find that by constraining the output to suppress illegal transitions we can train a tagger with a cross-entropy loss twice as fast as a CRF with differences in F1 that are statistically insignificant, effectively eliminating the need for a CRF. We analyze the dynamics of tag co-occurrence to explain when these constraints are most effective and provide open source implementations of our tagger in both PyTorch and TensorFlow.",38940105,https://www.aclweb.org/anthology/2020.findings-emnlp.166 +WS-15,WS-15.17,Sparse Optimization for Unsupervised Extractive Summarization of Long Documents with the Frank-Wolfe Algorithm,Alicia Tsai|Laurent El Ghaoui,"We address the problem of unsupervised extractive document summarization, especially for long documents. We model the unsupervised problem as a sparse auto-regression one and approximate the resulting combinatorial problem via a convex, norm-constrained problem. We solve it using a dedicated Frank-Wolfe algorithm. To generate a summary with k sentences, the algorithm only needs to execute approximately k iterations, making it very efficient for a long document. We evaluate our approach against two other unsupervised methods using both lexical (standard) ROUGE scores, as well as semantic (embedding-based) ones. Our method achieves better results with both datasets and works especially well when combined with embeddings for highly paraphrased summaries.",38939430,https://www.aclweb.org/anthology/2020.sustainlp-1.8 WS-15,WS-15.1887,Semi-supervised Formality Style Transfer using LanguageModel Discriminator and Mutual Information Maximization,Kunal Chawla,,38940140, -WS-15,WS-15.2,Rank and run-time aware compression of NLP Applications,Urmish Thakker|Jesse Beu|Dibakar Gope|Ganesh Dasika|Matthew Mattina,,38939420,https://www.aclweb.org/anthology/2020.sustainlp-1.2 -WS-15,WS-15.2015,Blockwise Self-Attention for Long Document Understanding,Jiezhong Qiu,,38940119,https://www.aclweb.org/anthology/2020.findings-emnlp.232 -WS-15,WS-15.2017,Unsupervised Few-Bits Semantic Hashing with Implicit Topics Modeling,Fanghua Ye,,38940106,https://www.aclweb.org/anthology/2020.findings-emnlp.233 +WS-15,WS-15.2,Rank and run-time aware compression of NLP Applications,Urmish Thakker|Jesse Beu|Dibakar Gope|Ganesh Dasika|Matthew Mattina,"Sequence model based NLP applications canbe large. Yet, many applications that benefit from them run on small devices with very limited compute and storage capabilities, while still having run-time constraints.As a result, there is a need for a compression technique that can achieve significant compression without negatively impacting inference run-time and task accuracy. This paper proposes a new compression technique called Hybrid Matrix Factorization (HMF) that achieves this dual objective. HMF improves low-rank matrix factorization (LMF) techniques by doubling the rank of the matrix using an intelligent hybrid-structure leading to better accuracy than LMF. Further, by preserving dense matrices, it leads to faster inference run-timethan pruning or structure matrix based compression technique. We evaluate the impact of this technique on 5 NLP benchmarks across multiple tasks (Translation, Intent Detection,Language Modeling) and show that for similar accuracy values and compression factors, HMF can achieve more than 2.32x faster inference run-time than pruning and 16.77% better accuracy than LMF.",38939420,https://www.aclweb.org/anthology/2020.sustainlp-1.2 +WS-15,WS-15.2015,Blockwise Self-Attention for Long Document Understanding,Jiezhong Qiu,"We present BlockBERT, a lightweight and efficient BERT model for better modeling long-distance dependencies. Our model extends BERT by introducing sparse block structures into the attention matrix to reduce both memory consumption and training/inference time, which also enables attention heads to capture either short- or long-range contextual information. We conduct experiments on language model pre-training and several benchmark question answering datasets with various paragraph lengths. BlockBERT uses 18.7-36.1% less memory and 12.0-25.1% less time to learn the model. During testing, BlockBERT saves 27.8% inference time, while having comparable and sometimes better prediction accuracy, compared to an advanced BERT-based model, RoBERTa.",38940119,https://www.aclweb.org/anthology/2020.findings-emnlp.232 +WS-15,WS-15.2017,Unsupervised Few-Bits Semantic Hashing with Implicit Topics Modeling,Fanghua Ye,"Semantic hashing is a powerful paradigm for representing texts as compact binary hash codes. The explosion of short text data has spurred the demand of few-bits hashing. However, the performance of existing semantic hashing methods cannot be guaranteed when applied to few-bits hashing because of severe information loss. In this paper, we present a simple but effective unsupervised neural generative semantic hashing method with a focus on few-bits hashing. Our model is built upon variational autoencoder and represents each hash bit as a Bernoulli variable, which allows the model to be end-to-end trainable. To address the issue of information loss, we introduce a set of auxiliary implicit topic vectors. With the aid of these topic vectors, the generated hash codes are not only low-dimensional representations of the original texts but also capture their implicit topics. We conduct comprehensive experiments on four datasets. The results demonstrate that our approach achieves significant improvements over state-of-the-art semantic hashing methods in few-bits hashing.",38940106,https://www.aclweb.org/anthology/2020.findings-emnlp.233 WS-15,WS-15.2182,DiPair: Fast and Accurate Distillation for Trillion-ScaleText Matching and Pair Modeling,Jiecao Chen,,38940104, -WS-15,WS-15.22,Don't Read Too Much Into It: Adaptive Computation for Open-Domain Question Answering,Yuxiang Wu|Pasquale Minervini|Pontus Stenetorp|Sebastian Riedel,,38939431,https://www.aclweb.org/anthology/2020.sustainlp-1.9 -WS-15,WS-15.2230,General Purpose Text Embeddings from Pre-trained Language Models for Scalable Inference,Myle Ott,,38940109,https://www.aclweb.org/anthology/2020.findings-emnlp.271 -WS-15,WS-15.2288,Domain Adversarial Fine-Tuning as an Effective Regularizer,Giorgos Vernikos,,38940129,https://www.aclweb.org/anthology/2020.findings-emnlp.278 -WS-15,WS-15.2453,Improve Transformer Models with Better Relative Position Embeddings,Zhiheng Huang,,38940108,https://www.aclweb.org/anthology/2020.findings-emnlp.298 -WS-15,WS-15.2516,Identifying Spurious Correlations for Robust Text Classification,Zhao Wang,,38940117,https://www.aclweb.org/anthology/2020.findings-emnlp.308 +WS-15,WS-15.22,Don't Read Too Much Into It: Adaptive Computation for Open-Domain Question Answering,Yuxiang Wu|Pasquale Minervini|Pontus Stenetorp|Sebastian Riedel,"Most approaches to Open-Domain Question Answering consist of a light-weight retriever that selects a set of candidate passages, and a computationally expensive reader that examines the passages to identify the correct answer. Previous works have shown that as the number of retrieved passages increases, so does the performance of the reader. However, they assume all retrieved passages are of equal importance and allocate the same amount of computation to them, leading to a substantial increase in computational cost. To reduce this cost, we propose the use of adaptive computation to control the computational budget allocated for the passages to be read. We first introduce a technique operating on individual passages in isolation which relies on anytime prediction and a per-layer estimation of an early exit probability. We then introduce SKYLINEBUILDER, an approach for dynamically deciding on which passage to allocate computation at each step, based on a resource allocation policy trained via reinforcement learning. Our results on SQuAD-Open show that adaptive computation with global prioritisation improves over several strong static and adaptive methods, leading to a 4.3x reduction in computation while retaining 95% performance of the full model.",38939431,https://www.aclweb.org/anthology/2020.sustainlp-1.9 +WS-15,WS-15.2230,General Purpose Text Embeddings from Pre-trained Language Models for Scalable Inference,Myle Ott,"The state of the art on many NLP tasks is currently achieved by large pre-trained language models, which require a considerable amount of computation. We aim to reduce the inference cost in a setting where many different predictions are made on a single piece of text. In that case, computational cost during inference can be amortized over the different predictions (tasks) using a shared text encoder. We compare approaches for training such an encoder and show that encoders pre-trained over multiple tasks generalize well to unseen tasks. We also compare ways of extracting fixed- and limited-size representations from this encoder, including pooling features extracted from multiple layers or positions. Our best approach compares favorably to knowledge distillation, achieving higher accuracy and lower computational cost once the system is handling around 7 tasks. Further, we show that through binary quantization, we can reduce the size of the extracted representations by a factor of 16 to store them for later use. The resulting method offers a compelling solution for using large-scale pre-trained models at a fraction of the computational cost when multiple tasks are performed on the same text.",38940109,https://www.aclweb.org/anthology/2020.findings-emnlp.271 +WS-15,WS-15.2288,Domain Adversarial Fine-Tuning as an Effective Regularizer,Giorgos Vernikos,"In Natural Language Processing (NLP), pretrained language models (LMs) that are transferred to downstream tasks have been recently shown to achieve state-of-the-art results. However, standard fine-tuning can degrade the general-domain representations captured during pretraining. To address this issue, we introduce a new regularization technique, AFTER; domain Adversarial Fine-Tuning as an Effective Regularizer. Specifically, we complement the task-specific loss used during fine-tuning with an adversarial objective. This additional loss term is related to an adversarial classifier, that aims to discriminate between in-domain and out-of-domain text representations. Indomain refers to the labeled dataset of the task at hand while out-of-domain refers to unlabeled data from a different domain. Intuitively, the adversarial classifier acts as a regularize which prevents the model from overfitting to the task-specific domain. Empirical results on various natural language understanding tasks show that AFTER leads to improved performance compared to standard fine-tuning.",38940129,https://www.aclweb.org/anthology/2020.findings-emnlp.278 +WS-15,WS-15.2453,Improve Transformer Models with Better Relative Position Embeddings,Zhiheng Huang,"The transformer model has demonstrated superior results on NLP tasks including machine translation and question answering. In this paper, we argue that the position information is not fully utilized in existing work. For example, the initial proposal of a sinusoid embedding is fixed and not learnable. In this paper, we first review the absolute position embeddings and existing relative position embedding methods. We then propose new methods to encourage increased interaction between query, key and relative position embeddings in the self-attention mechanism. Our most promising approach is a generalization of the absolute position embedding. Our method results in increased accuracy compared to previous approaches in absolute and relative position embeddings on the SQuAD1.1 dataset. In addition, we address the inductive property of whether a position embedding can be robust enough to handle long sequences. We demonstrate empirically that our relative embedding method can be reasonably generalized to and is robust in the inductive perspective. Finally, we show that our proposed method can be effectively and efficiently adopted as a near drop-in replacement for improving the accuracy of large models with little computational overhead.",38940108,https://www.aclweb.org/anthology/2020.findings-emnlp.298 +WS-15,WS-15.2516,Identifying Spurious Correlations for Robust Text Classification,Zhao Wang,"The predictions of text classifiers are often driven by spurious correlations – e.g., the term “Spielberg” correlates with positively reviewed movies, even though the term itself does not semantically convey a positive sentiment. In this paper, we propose a method to distinguish spurious and genuine correlations in text classification. We treat this as a supervised classification problem, using features derived from treatment effect estimators to distinguish spurious correlations from “genuine” ones. Due to the generic nature of these features and their small dimensionality, we find that the approach works well even with limited training examples, and that it is possible to transport the word classifier to new domains. Experiments on four datasets (sentiment classification and toxicity detection) suggest that using this approach to inform feature selection also leads to more robust classification, as measured by improved worst-case accuracy on the samples affected by spurious correlations.",38940117,https://www.aclweb.org/anthology/2020.findings-emnlp.308 WS-15,WS-15.27,Doped Structured Matrices for Extreme Compression of LSTM Models,Urmish Thakker,,38940744, -WS-15,WS-15.28,A Two-stage Model for Slot Filling in Low-resource Settings: Domain-agnostic Non-slot Reduction and Pretrained Contextual Embeddings,Cennet Oguz|Ngoc Thang Vu,,38939432,https://www.aclweb.org/anthology/2020.sustainlp-1.10 -WS-15,WS-15.29,Early Exiting BERT for Efficient Document Ranking,Ji Xin|Rodrigo Nogueira|Yaoliang Yu|Jimmy Lin,,38939433,https://www.aclweb.org/anthology/2020.sustainlp-1.11 +WS-15,WS-15.28,A Two-stage Model for Slot Filling in Low-resource Settings: Domain-agnostic Non-slot Reduction and Pretrained Contextual Embeddings,Cennet Oguz|Ngoc Thang Vu,"Learning-based slot filling - a key component of spoken language understanding systems - typically requires a large amount of in-domain hand-labeled data for training. In this paper, we propose a novel two-stage model architecture that can be trained with only a few in-domain hand-labeled examples. The first step is designed to remove non-slot tokens (i.e., O labeled tokens), as they introduce noise in the input of slot filling models. This step is domain-agnostic and therefore, can be trained by exploiting out-of-domain data. The second step identifies slot names only for slot tokens by using state-of-the-art pretrained contextual embeddings such as ELMO and BERT. We show that our approach outperforms other state-of-art systems on the SNIPS benchmark dataset.",38939432,https://www.aclweb.org/anthology/2020.sustainlp-1.10 +WS-15,WS-15.29,Early Exiting BERT for Efficient Document Ranking,Ji Xin|Rodrigo Nogueira|Yaoliang Yu|Jimmy Lin,"Pre-trained language models such as BERT have shown their effectiveness in various tasks. Despite their power, they are known to be computationally intensive, which hinders real-world applications. In this paper, we introduce early exiting BERT for document ranking. With a slight modification, BERT becomes a model with multiple output paths, and each inference sample can exit early from these paths. In this way, computation can be effectively allocated among samples, and overall system latency is significantly reduced while the original quality is maintained. Our experiments on two document ranking datasets demonstrate up to 2.5x inference speedup with minimal quality degradation. The source code of our implementation can be found at https://github.com/castorini/earlyexiting-monobert.",38939433,https://www.aclweb.org/anthology/2020.sustainlp-1.11 WS-15,WS-15.3,Incremental Neural Coreference Resolution in Constant Memory,Patrick Xia|João Sedoc|Benjamin Van Durme,,38939421, -WS-15,WS-15.30,Keyphrase Generation with GANs in Low-Resources Scenarios,Giuseppe Lancioni|Saida S.Mohamed|Beatrice Portelli|Giuseppe Serra|Carlo Tasso,,38939434,https://www.aclweb.org/anthology/2020.sustainlp-1.12 -WS-15,WS-15.3078,SupMMD: A Sentence Importance Model for Extractive Summarization using Maximum Mean Discrepancy,Umanga Bista,,38940131,https://www.aclweb.org/anthology/2020.findings-emnlp.367 -WS-15,WS-15.32,Quasi-Multitask Learning: an Efficient Surrogate for Obtaining Model Ensembles,Norbert Kis-Szabó|Gábor Berend,,38939435,https://www.aclweb.org/anthology/2020.sustainlp-1.13 -WS-15,WS-15.34,A Little Bit Is Worse Than None: Ranking with Limited Training Data,Xinyu Zhang|Andrew Yates|Jimmy Lin,,38939436,https://www.aclweb.org/anthology/2020.sustainlp-1.14 -WS-15,WS-15.3444,Multi-hop Question Generation with Graph Convolutional Network,Dan Su,,38940120,https://www.aclweb.org/anthology/2020.findings-emnlp.416 -WS-15,WS-15.3459,Guiding Attention for Self-Supervised Learning with Transformers,Ameet Deshpande,,38940124,https://www.aclweb.org/anthology/2020.findings-emnlp.419 +WS-15,WS-15.30,Keyphrase Generation with GANs in Low-Resources Scenarios,Giuseppe Lancioni|Saida S.Mohamed|Beatrice Portelli|Giuseppe Serra|Carlo Tasso,"Keyphrase Generation is the task of predicting Keyphrases (KPs), short phrases that summarize the semantic meaning of a given document. Several past studies provided diverse approaches to generate Keyphrases for an input document. However, all of these approaches still need to be trained on very large datasets. In this paper, we introduce BeGanKP, a new conditional GAN model to address the problem of Keyphrase Generation in a low-resource scenario. Our main contribution relies in the Discriminator’s architecture: a new BERT-based module which is able to distinguish between the generated and humancurated KPs reliably. Its characteristics allow us to use it in a low-resource scenario, where only a small amount of training data are available, obtaining an efficient Generator. The resulting architecture achieves, on five public datasets, competitive results with respect to the state-of-the-art approaches, using less than 1% of the training data.",38939434,https://www.aclweb.org/anthology/2020.sustainlp-1.12 +WS-15,WS-15.3078,SupMMD: A Sentence Importance Model for Extractive Summarization using Maximum Mean Discrepancy,Umanga Bista,"Most work on multi-document summarization has focused on generic summarization of information present in each individual document set. However, the under-explored setting of update summarization, where the goal is to identify the new information present in each set, is of equal practical interest (e.g., presenting readers with updates on an evolving news topic). In this work, we present SupMMD, a novel technique for generic and update summarization based on the maximum mean discrepancy from kernel two-sample testing. SupMMD combines both supervised learning for salience and unsupervised learning for coverage and diversity. Further, we adapt multiple kernel learning to make use of similarity across multiple information sources (e.g., text features and knowledge based concepts). We show the efficacy of SupMMD in both generic and update summarization tasks by meeting or exceeding the current state-of-the-art on the DUC-2004 and TAC-2009 datasets.",38940131,https://www.aclweb.org/anthology/2020.findings-emnlp.367 +WS-15,WS-15.32,Quasi-Multitask Learning: an Efficient Surrogate for Obtaining Model Ensembles,Norbert Kis-Szabó|Gábor Berend,"We propose the technique of quasi-multitask learning (Q-MTL), a simple and easy to implement modification of standard multitask learning, in which the tasks to be modeled are identical. With this easy modification of a standard neural classifier we can get benefits similar to an ensemble of classifiers with a fraction of the resources required.We illustrate it through a series of sequence labeling experiments over a diverse set of languages, that applying Q-MTL consistently increases the generalization ability of the applied models. The proposed architecture can be regarded as a new regularization technique that encourages the model to develop an internal representation of the problem at hand which is beneficial to multiple output units of the classifier at the same time. Our experiments corroborate that by relying on the proposed algorithm, we can approximate the quality of an ensemble of classifiers at a fraction of computational resources required. Additionally, our results suggest that Q-MTL handles the presence of noisy training labels better than ensembles.",38939435,https://www.aclweb.org/anthology/2020.sustainlp-1.13 +WS-15,WS-15.34,A Little Bit Is Worse Than None: Ranking with Limited Training Data,Xinyu Zhang|Andrew Yates|Jimmy Lin,"Researchers have proposed simple yet effective techniques for the retrieval problem based on using BERT as a relevance classifier to rerank initial candidates from keyword search. In this work, we tackle the challenge of fine-tuning these models for specific domains in a data and computationally efficient manner. Typically, researchers fine-tune models using corpus-specific labeled data from sources such as TREC. We first answer the question: How much data of this type do we need? Recognizing that the most computationally efficient training is no training, we explore zero-shot ranking using BERT models that have already been fine-tuned with the large MS MARCO passage retrieval dataset. We arrive at the surprising and novel finding that “some” labeled in-domain data can be worse than none at all.",38939436,https://www.aclweb.org/anthology/2020.sustainlp-1.14 +WS-15,WS-15.3444,Multi-hop Question Generation with Graph Convolutional Network,Dan Su,"Multi-hop Question Generation (QG) aims to generate answer-related questions by aggregating and reasoning over multiple scattered evidence from different paragraphs. It is a more challenging yet under-explored task compared to conventional single-hop QG, where the questions are generated from the sentence containing the answer or nearby sentences in the same paragraph without complex reasoning. To address the additional challenges in multi-hop QG, we propose Multi-Hop Encoding Fusion Network for Question Generation (MulQG), which does context encoding in multiple hops with Graph Convolutional Network and encoding fusion via an Encoder Reasoning Gate. To the best of our knowledge, we are the first to tackle the challenge of multi-hop reasoning over paragraphs without any sentence-level information. Empirical results on HotpotQA dataset demonstrate the effectiveness of our method, in comparison with baselines on automatic evaluation metrics. Moreover, from the human evaluation, our proposed model is able to generate fluent questions with high completeness and outperforms the strongest baseline by 20.8% in the multi-hop evaluation. on. The code is publicly availableat https://github.com/HLTCHKU",38940120,https://www.aclweb.org/anthology/2020.findings-emnlp.416 +WS-15,WS-15.3459,Guiding Attention for Self-Supervised Learning with Transformers,Ameet Deshpande,"In this paper, we propose a simple and effective technique to allow for efficient self-supervised learning with bi-directional Transformers. Our approach is motivated by recent studies demonstrating that self-attention patterns in trained models contain a majority of non-linguistic regularities. We propose a computationally efficient auxiliary loss function to guide attention heads to conform to such patterns. Our method is agnostic to the actual pre-training objective and results in faster convergence of models as well as better performance on downstream tasks compared to the baselines, achieving state of the art results in low-resource settings. Surprisingly, we also find that linguistic properties of attention heads are not necessarily correlated with language modeling performance.",38940124,https://www.aclweb.org/anthology/2020.findings-emnlp.419 WS-15,WS-15.3526,Probabilstic Case-based Reasoning for Open-World Knowledge Graph Completion,Rajarshi Das,,38940133, -WS-15,WS-15.3562,Extremely Low Bit Transformer Quantization for On-Device Neural Machine Translation,Insoo Chung,,38940118,https://www.aclweb.org/anthology/2020.findings-emnlp.433 -WS-15,WS-15.36,Predictive Model Selection for Transfer Learning in Sequence Labeling Tasks,Parul Awasthy|Bishwaranjan Bhattacharjee|John Kender|Radu Florian,,38939437,https://www.aclweb.org/anthology/2020.sustainlp-1.15 -WS-15,WS-15.361,Understanding tables with intermediate pre-training,Julian Martin Eisenschlos,,38940134,https://www.aclweb.org/anthology/2020.findings-emnlp.27 -WS-15,WS-15.37,Load What You Need: Smaller Versions of Mutlilingual BERT,Amine Abdaoui|Camille Pradel|Grégoire Sigel,,38939438,https://www.aclweb.org/anthology/2020.sustainlp-1.16 -WS-15,WS-15.38,SqueezeBERT: What can computer vision teach NLP about efficient neural networks?,Forrest Iandola|Albert Shaw|Ravi Krishna|Kurt Keutzer,,38939439,https://www.aclweb.org/anthology/2020.sustainlp-1.17 -WS-15,WS-15.381,Enhance Robustness of Sequence Labelling with Masked Adversarial Training,Luoxin Chen,,38940127,https://www.aclweb.org/anthology/2020.findings-emnlp.28 -WS-15,WS-15.39,Analysis of Resource-efficient Predictive Models for Natural Language Processing,Raj Pranesh|Ambesh Shekhar,,38939440,https://www.aclweb.org/anthology/2020.sustainlp-1.18 -WS-15,WS-15.42,Towards Accurate and Reliable Energy Measurement of NLP Models,Qingqing Cao|Aruna Balasubramanian|Niranjan Balasubramanian,,38939441,https://www.aclweb.org/anthology/2020.sustainlp-1.19 -WS-15,WS-15.43,FastFormers: Highly Efficient Transformer Models for Natural Language Understanding,Young Jin Kim|Hany Hassan,,38939442,https://www.aclweb.org/anthology/2020.sustainlp-1.20 -WS-15,WS-15.45,A comparison between CNNs and WFAs for Sequence Classification,Ariadna Quattoni|Xavier Carreras,,38939443,https://www.aclweb.org/anthology/2020.sustainlp-1.21 -WS-15,WS-15.46,Counterfactual Augmentation for Training Next Response Selection,Seungtaek Choi|Myeongho Jeong|Jinyoung Yeo|Seung-won Hwang,,38939444,https://www.aclweb.org/anthology/2020.sustainlp-1.22 -WS-15,WS-15.47,Do We Need to Create Big Datasets to Learn a Task?,Swaroop Mishra|Bhavdeep Singh Sachdeva,,38939445,https://www.aclweb.org/anthology/2020.sustainlp-1.23 -WS-15,WS-15.49,Guiding Attention for Self-Supervised Learning with Transformers,Ameet Deshpande|Karthik Narasimhan,,38939446,https://www.aclweb.org/anthology/2020.findings-emnlp.419 -WS-15,WS-15.5,Learning Informative Representations of Biomedical Relations with Latent Variable Models,Harshil Shah|Julien Fauqueur,,38939422,https://www.aclweb.org/anthology/2020.sustainlp-1.3 -WS-15,WS-15.512,Fixed Encoder Self-Attention Patterns in Transformer-Based Machine Translation,Alessandro Raganato,,38940110,https://www.aclweb.org/anthology/2020.findings-emnlp.49 -WS-15,WS-15.547,PBoS: Probabilistic Bag-of-Subwords for Generalizing Word Embedding,Zhao Jinman,,38940115,https://www.aclweb.org/anthology/2020.findings-emnlp.53 -WS-15,WS-15.6,End to End Binarized Neural Networks for Text Classification,Kumar Shridhar|Harshil Jain|Akshat Agarwal|Denis Kleyko,,38939423,https://www.aclweb.org/anthology/2020.sustainlp-1.4 -WS-15,WS-15.651,Pruning Redundant Mappings in Transformer Models via Spectral-Normalized Identity Prior,Zi Lin,,38940112,https://www.aclweb.org/anthology/2020.findings-emnlp.64 -WS-15,WS-15.724,Improving QA Generalization by Concurrent Modeling of Multiple Biases,Mingzhu Wu,,38940113,https://www.aclweb.org/anthology/2020.findings-emnlp.74 -WS-15,WS-15.8,Large Product Key Memory for Pre-trained Language Models,Gyuwan Kim|Tae Hwan Jung,,38939424,https://www.aclweb.org/anthology/2020.findings-emnlp.362 +WS-15,WS-15.3562,Extremely Low Bit Transformer Quantization for On-Device Neural Machine Translation,Insoo Chung,"The deployment of widely used Transformer architecture is challenging because of heavy computation load and memory overhead during inference, especially when the target device is limited in computational resources such as mobile or edge devices. Quantization is an effective technique to address such challenges. Our analysis shows that for a given number of quantization bits, each block of Transformer contributes to translation quality and inference computations in different manners. Moreover, even inside an embedding block, each word presents vastly different contributions. Correspondingly, we propose a mixed precision quantization strategy to represent Transformer weights by an extremely low number of bits (e.g., under 3 bits). For example, for each word in an embedding block, we assign different quantization bits based on statistical property. Our quantized Transformer model achieves 11.8× smaller model size than the baseline model, with less than -0.5 BLEU. We achieve 8.3× reduction in run-time memory footprints and 3.5× speed up (Galaxy N10+) such that our proposed compression strategy enables efficient implementation for on-device NMT.",38940118,https://www.aclweb.org/anthology/2020.findings-emnlp.433 +WS-15,WS-15.36,Predictive Model Selection for Transfer Learning in Sequence Labeling Tasks,Parul Awasthy|Bishwaranjan Bhattacharjee|John Kender|Radu Florian,"Transfer learning is a popular technique to learn a task using less training data and fewer compute resources. However, selecting the correct source model for transfer learning is a challenging task. We demonstrate a novel predictive method that determines which existing source model would minimize error for transfer learning to a given target. This technique does not require learning for prediction, and avoids computational costs of trail-and-error. We have evaluated this technique on nine datasets across diverse domains, including newswire, user forums, air flight booking, cybersecurity news, etc. We show that it per-forms better than existing techniques such as fine-tuning over vanilla BERT, or curriculum learning over the largest dataset on top of BERT, resulting in average F1 score gains in excess of 3%. Moreover, our technique consistently selects the best model using fewer tries.",38939437,https://www.aclweb.org/anthology/2020.sustainlp-1.15 +WS-15,WS-15.361,Understanding tables with intermediate pre-training,Julian Martin Eisenschlos,"Table entailment, the binary classification task of finding if a sentence is supported or refuted by the content of a table, requires parsing language and table structure as well as numerical and discrete reasoning. While there is extensive work on textual entailment, table entailment is less well studied. We adapt TAPAS (Herzig et al., 2020), a table-based BERT model, to recognize entailment. Motivated by the benefits of data augmentation, we create a balanced dataset of millions of automatically created training examples which are learned in an intermediate step prior to fine-tuning. This new data is not only useful for table entailment, but also for SQA (Iyyer et al., 2017), a sequential table QA task. To be able to use long examples as input of BERT models, we evaluate table pruning techniques as a pre-processing step to drastically improve the training and prediction efficiency at a moderate drop in accuracy. The different methods set the new state-of-the-art on the TabFact (Chen et al., 2020) and SQA datasets.",38940134,https://www.aclweb.org/anthology/2020.findings-emnlp.27 +WS-15,WS-15.37,Load What You Need: Smaller Versions of Mutlilingual BERT,Amine Abdaoui|Camille Pradel|Grégoire Sigel,"Pre-trained Transformer-based models are achieving state-of-the-art results on a variety of Natural Language Processing data sets. However, the size of these models is often a drawback for their deployment in real production applications. In the case of multilingual models, most of the parameters are located in the embeddings layer. Therefore, reducing the vocabulary size should have an important impact on the total number of parameters. In this paper, we propose to extract smaller models that handle fewer number of languages according to the targeted corpora. We present an evaluation of smaller versions of multilingual BERT on the XNLI data set, but we believe that this method may be applied to other multilingual transformers. The obtained results confirm that we can generate smaller models that keep comparable results, while reducing up to 45% of the total number of parameters. We compared our models with DistilmBERT (a distilled version of multilingual BERT) and showed that unlike language reduction, distillation induced a 1.7% to 6% drop in the overall accuracy on the XNLI data set. The presented models and code are publicly available.",38939438,https://www.aclweb.org/anthology/2020.sustainlp-1.16 +WS-15,WS-15.38,SqueezeBERT: What can computer vision teach NLP about efficient neural networks?,Forrest Iandola|Albert Shaw|Ravi Krishna|Kurt Keutzer,"Humans read and write hundreds of billions of messages every day. Further, due to the availability of large datasets, large computing systems, and better neural network models, natural language processing (NLP) technology has made significant strides in understanding, proofreading, and organizing these messages. Thus, there is a significant opportunity to deploy NLP in myriad applications to help web users, social networks, and businesses. Toward this end, we consider smartphones and other mobile devices as crucial platforms for deploying NLP models at scale. However, today’s highly-accurate NLP neural network models such as BERT and RoBERTa are extremely computationally expensive, with BERT-base taking 1.7 seconds to classify a text snippet on a Pixel 3 smartphone. To begin to address this problem, we draw inspiration from the computer vision community, where work such as MobileNet has demonstrated that grouped convolutions (e.g. depthwise convolutions) can enable speedups without sacrificing accuracy. We demonstrate how to replace several operations in self-attention layers with grouped convolutions, and we use this technique in a novel network architecture called SqueezeBERT, which runs 4.3x faster than BERT-base on the Pixel 3 while achieving competitive accuracy on the GLUE test set. A PyTorch-based implementation of SqueezeBERT is available as part of the Hugging Face Transformers library: https://huggingface.co/squeezebert",38939439,https://www.aclweb.org/anthology/2020.sustainlp-1.17 +WS-15,WS-15.381,Enhance Robustness of Sequence Labelling with Masked Adversarial Training,Luoxin Chen,"Adversarial training (AT) has shown strong regularization effects on deep learning algorithms by introducing small input perturbations to improve model robustness. In language tasks, adversarial training brings word-level robustness by adding input noise, which is beneficial for text classification. However, it lacks sufficient contextual information enhancement and thus is less useful for sequence labelling tasks such as chunking and named entity recognition (NER). To address this limitation, we propose masked adversarial training (MAT) to improve robustness from contextual information in sequence labelling. MAT masks or replaces some words in the sentence when computing adversarial loss from perturbed inputs and consequently enhances model robustness using more context-level information. In our experiments, our method shows significant improvements on accuracy and robustness of sequence labelling. By further incorporating with ELMo embeddings, our model achieves better or comparable results to state-of-the-art on CoNLL 2000 and 2003 benchmarks using much less parameters.",38940127,https://www.aclweb.org/anthology/2020.findings-emnlp.28 +WS-15,WS-15.39,Analysis of Resource-efficient Predictive Models for Natural Language Processing,Raj Pranesh|Ambesh Shekhar,"In this paper, we presented an analyses of the resource efficient predictive models, namely Bonsai, Binary Neighbor Compression(BNC), ProtoNN, Random Forest, Naive Bayes and Support vector machine(SVM), in the machine learning field for resource constraint devices. These models try to minimize resource requirements like RAM and storage without hurting the accuracy much. We utilized these models on multiple benchmark natural language processing tasks, which were sentimental analysis, spam message detection, emotion analysis and fake news classification. The experiment results shows that the tree-based algorithm, Bonsai, surpassed the rest of the machine learning algorithms by achieve higher accuracy scores while having significantly lower memory usage.",38939440,https://www.aclweb.org/anthology/2020.sustainlp-1.18 +WS-15,WS-15.42,Towards Accurate and Reliable Energy Measurement of NLP Models,Qingqing Cao|Aruna Balasubramanian|Niranjan Balasubramanian,"Accurate and reliable measurement of energy consumption is critical for making well-informed design choices when choosing and training large scale NLP models. In this work, we show that existing software-based energy estimations are not accurate because they do not take into account hardware differences and how resource utilization affects energy consumption. We conduct energy measurement experiments with four different models for a question answering task. We quantify the error of existing software-based energy estimations by using a hardware power meter that provides highly accurate energy measurements. Our key takeaway is the need for a more accurate energy estimation model that takes into account hardware variabilities and the non-linear relationship between resource utilization and energy consumption. We release the code and data at https://github.com/csarron/sustainlp2020-energy.",38939441,https://www.aclweb.org/anthology/2020.sustainlp-1.19 +WS-15,WS-15.43,FastFormers: Highly Efficient Transformer Models for Natural Language Understanding,Young Jin Kim|Hany Hassan,"Transformer-based models are the state-of-the-art for Natural Language Understanding (NLU) applications. Models are getting bigger and better on various tasks. However, Transformer models remain computationally challenging since they are not efficient at inference-time compared to traditional approaches. In this paper, we present FastFormers, a set of recipes to achieve efficient inference-time performance for Transformer-based models on various NLU tasks. We show how carefully utilizing knowledge distillation, structured pruning and numerical optimization can lead to drastic improvements on inference efficiency. We provide effective recipes that can guide practitioners to choose the best settings for various NLU tasks and pretrained models. Applying the proposed recipes to the SuperGLUE benchmark, we achieve from 9.8x up to 233.9x speed-up compared to out-of-the-box models on CPU. On GPU, we also achieve up to 12.4x speed-up with the presented methods. We show that FastFormers can drastically reduce cost of serving 100 million requests from 4,223 USD to just 18 USD on an Azure F16s_v2 instance. This translates to a sustainable runtime by reducing energy consumption 6.9x - 125.8x according to the metrics used in the SustaiNLP 2020 shared task.",38939442,https://www.aclweb.org/anthology/2020.sustainlp-1.20 +WS-15,WS-15.45,A comparison between CNNs and WFAs for Sequence Classification,Ariadna Quattoni|Xavier Carreras,"We compare a classical CNN architecture for sequence classification involving several convolutional and max-pooling layers against a simple model based on weighted finite state automata (WFA). Each model has its advantages and disadvantages and it is possible that they could be combined. However, we believe that the first research goal should be to investigate and understand how do these two apparently dissimilar models compare in the context of specific natural language processing tasks. This paper is the first step towards that goal. Our experiments with five sequence classification datasets suggest that, despite the apparent simplicity of WFA models and training algorithms, the performance of WFAs is comparable to that of the CNNs.",38939443,https://www.aclweb.org/anthology/2020.sustainlp-1.21 +WS-15,WS-15.46,Counterfactual Augmentation for Training Next Response Selection,Seungtaek Choi|Myeongho Jeong|Jinyoung Yeo|Seung-won Hwang,"This paper studies label augmentation for training dialogue response selection. The existing model is trained by “observational” annotation, where one observed response is annotated as gold. In this paper, we propose “counterfactual augmentation” of pseudo-positive labels. We validate that the effectiveness of augmented labels are comparable to positives, such that ours outperform state-of-the-arts without augmentation.",38939444,https://www.aclweb.org/anthology/2020.sustainlp-1.22 +WS-15,WS-15.47,Do We Need to Create Big Datasets to Learn a Task?,Swaroop Mishra|Bhavdeep Singh Sachdeva,"Deep Learning research has been largely accelerated by the development of huge datasets such as Imagenet. The general trend has been to create big datasets to make a deep neural network learn. A huge amount of resources is being spent in creating these big datasets, developing models, training them, and iterating this process to dominate leaderboards. We argue that the trend of creating bigger datasets needs to be revised by better leveraging the power of pre-trained language models. Since the language models have already been pre-trained with huge amount of data and have basic linguistic knowledge, there is no need to create big datasets to learn a task. Instead, we need to create a dataset that is sufficient for the model to learn various task-specific terminologies, such as ‘Entailment’, ‘Neutral’, and ‘Contradiction’ for NLI. As evidence, we show that RoBERTA is able to achieve near-equal performance on 2% data of SNLI. We also observe competitive zero-shot generalization on several OOD datasets. In this paper, we propose a baseline algorithm to find the optimal dataset for learning a task.",38939445,https://www.aclweb.org/anthology/2020.sustainlp-1.23 +WS-15,WS-15.49,Guiding Attention for Self-Supervised Learning with Transformers,Ameet Deshpande|Karthik Narasimhan,"In this paper, we propose a simple and effective technique to allow for efficient self-supervised learning with bi-directional Transformers. Our approach is motivated by recent studies demonstrating that self-attention patterns in trained models contain a majority of non-linguistic regularities. We propose a computationally efficient auxiliary loss function to guide attention heads to conform to such patterns. Our method is agnostic to the actual pre-training objective and results in faster convergence of models as well as better performance on downstream tasks compared to the baselines, achieving state of the art results in low-resource settings. Surprisingly, we also find that linguistic properties of attention heads are not necessarily correlated with language modeling performance.",38939446,https://www.aclweb.org/anthology/2020.findings-emnlp.419 +WS-15,WS-15.5,Learning Informative Representations of Biomedical Relations with Latent Variable Models,Harshil Shah|Julien Fauqueur,"Extracting biomedical relations from large corpora of scientific documents is a challenging natural language processing task. Existing approaches usually focus on identifying a relation either in a single sentence (mention-level) or across an entire corpus (pair-level). In both cases, recent methods have achieved strong results by learning a point estimate to represent the relation; this is then used as the input to a relation classifier. However, the relation expressed in text between a pair of biomedical entities is often more complex than can be captured by a point estimate. To address this issue, we propose a latent variable model with an arbitrarily flexible distribution to represent the relation between an entity pair. Additionally, our model provides a unified architecture for both mention-level and pair-level relation extraction. We demonstrate that our model achieves results competitive with strong baselines for both tasks while having fewer parameters and being significantly faster to train. We make our code publicly available.",38939422,https://www.aclweb.org/anthology/2020.sustainlp-1.3 +WS-15,WS-15.512,Fixed Encoder Self-Attention Patterns in Transformer-Based Machine Translation,Alessandro Raganato,"Transformer-based models have brought a radical change to neural machine translation. A key feature of the Transformer architecture is the so-called multi-head attention mechanism, which allows the model to focus simultaneously on different parts of the input. However, recent works have shown that most attention heads learn simple, and often redundant, positional patterns. In this paper, we propose to replace all but one attention head of each encoder layer with simple fixed – non-learnable – attentive patterns that are solely based on position and do not require any external knowledge. Our experiments with different data sizes and multiple language pairs show that fixing the attention heads on the encoder side of the Transformer at training time does not impact the translation quality and even increases BLEU scores by up to 3 points in low-resource scenarios.",38940110,https://www.aclweb.org/anthology/2020.findings-emnlp.49 +WS-15,WS-15.547,PBoS: Probabilistic Bag-of-Subwords for Generalizing Word Embedding,Zhao Jinman,"We look into the task of generalizing word embeddings: given a set of pre-trained word vectors over a finite vocabulary, the goal is to predict embedding vectors for out-of-vocabulary words, without extra contextual information. We rely solely on the spellings of words and propose a model, along with an efficient algorithm, that simultaneously models subword segmentation and computes subword-based compositional word embedding. We call the model probabilistic bag-of-subwords (PBoS), as it applies bag-of-subwords for all possible segmentations based on their likelihood. Inspections and affix prediction experiment show that PBoS is able to produce meaningful subword segmentations and subword rankings without any source of explicit morphological knowledge. Word similarity and POS tagging experiments show clear advantages of PBoS over previous subword-level models in the quality of generated word embeddings across languages.",38940115,https://www.aclweb.org/anthology/2020.findings-emnlp.53 +WS-15,WS-15.6,End to End Binarized Neural Networks for Text Classification,Kumar Shridhar|Harshil Jain|Akshat Agarwal|Denis Kleyko,"Deep neural networks have demonstrated their superior performance in almost every Natural Language Processing task, however, their increasing complexity raises concerns. A particular concern is that these networks pose high requirements for computing hardware and training budgets. The state-of-the-art transformer models are a vivid example. Simplifying the computations performed by a network is one way of addressing the issue of the increasing complexity. In this paper, we propose an end to end binarized neural network for the task of intent and text classification. In order to fully utilize the potential of end to end binarization, both the input representations (vector embeddings of tokens statistics) and the classifier are binarized. We demonstrate the efficiency of such a network on the intent classification of short texts over three datasets and text classification with a larger dataset. On the considered datasets, the proposed network achieves comparable to the state-of-the-art results while utilizing 20-40% lesser memory and training time compared to the benchmarks.",38939423,https://www.aclweb.org/anthology/2020.sustainlp-1.4 +WS-15,WS-15.651,Pruning Redundant Mappings in Transformer Models via Spectral-Normalized Identity Prior,Zi Lin,"Traditional (unstructured) pruning methods for a Transformer model focus on regularizing the individual weights by penalizing them toward zero. In this work, we explore spectral-normalized identity priors (SNIP), a structured pruning approach which penalizes an entire residual module in a Transformer model toward an identity mapping. Our method identifies and discards unimportant non-linear mappings in the residual connections by applying a thresholding operator on the function norm, and is applicable to any structured module including a single attention head, an entire attention blocks, or a feed-forward subnetwork. Furthermore, we introduce spectral normalization to stabilize the distribution of the post-activation values of the Transformer layers, further improving the pruning effectiveness of the proposed methodology. We conduct experiments with BERT on 5 GLUE benchmark tasks to demonstrate that SNIP achieves effective pruning results while maintaining comparable performance. Specifically, we improve the performance over the state-of-the-art by 0.5 to 1.0% on average at 50% compression ratio.",38940112,https://www.aclweb.org/anthology/2020.findings-emnlp.64 +WS-15,WS-15.724,Improving QA Generalization by Concurrent Modeling of Multiple Biases,Mingzhu Wu,"Existing NLP datasets contain various biases that models can easily exploit to achieve high performances on the corresponding evaluation sets. However, focusing on dataset-specific biases limits their ability to learn more generalizable knowledge about the task from more general data patterns. In this paper, we investigate the impact of debiasing methods for improving generalization and propose a general framework for improving the performance on both in-domain and out-of-domain datasets by concurrent modeling of multiple biases in the training data. Our framework weights each example based on the biases it contains and the strength of those biases in the training data. It then uses these weights in the training objective so that the model relies less on examples with high bias weights. We extensively evaluate our framework on extractive question answering with training data from various domains with multiple biases of different strengths. We perform the evaluations in two different settings, in which the model is trained on a single domain or multiple domains simultaneously, and show its effectiveness in both settings compared to state-of-the-art debiasing methods.",38940113,https://www.aclweb.org/anthology/2020.findings-emnlp.74 +WS-15,WS-15.8,Large Product Key Memory for Pre-trained Language Models,Gyuwan Kim|Tae Hwan Jung,"Product key memory (PKM) proposed by Lample et al. (2019) enables to improve prediction accuracy by increasing model capacity efficiently with insignificant computational overhead. However, their empirical application is only limited to causal language modeling. Motivated by the recent success of pretrained language models (PLMs), we investigate how to incorporate large PKM into PLMs that can be finetuned for a wide variety of downstream NLP tasks. We define a new memory usage metric, and careful observation using this metric reveals that most memory slots remain outdated during the training of PKM-augmented models. To train better PLMs by tackling this issue, we propose simple but effective solutions: (1) initialization from the model weights pretrained without memory and (2) augmenting PKM by addition rather than replacing a feed-forward network. We verify that both of them are crucial for the pretraining of PKM-augmented PLMs, enhancing memory utilization and downstream performance. Code and pretrained weights are available at https://github.com/clovaai/pkm-transformers.",38939424,https://www.aclweb.org/anthology/2020.findings-emnlp.362 WS-15,WS-15.9,P-SIF: Document Embeddings using Partition Averaging,Vivek Gupta|Ankit Saw|Pegah Nokhiz|Praneeth Netrapalli|Piyush Rai|Partha Talukdar,,38939425, -WS-15,WS-15.929,ESTeR: Combining Word Co-occurrences and Word Associations for Unsupervised Emotion Detection,Polina Rozenshtein,,38940135,https://www.aclweb.org/anthology/2020.findings-emnlp.93 -WS-16,WS-16.10,Exploring Span Representations in Neural Coreference Resolution,Patrick Kahardipraja|Olena Vyshnevska|Sharid Loáiciga,,38939689,https://www.aclweb.org/anthology/2020.codi-1.4 -WS-16,WS-16.11,Supporting Comedy Writers: Predicting Audience's Response from Sketch Comedy and Crosstalk Scripts,Maolin Li,,38939690,https://www.aclweb.org/anthology/2020.codi-1.5 -WS-16,WS-16.13,Exploring Coreference Features in Heterogeneous Data with Text Classification,Ekaterina Lapshinova-Koltunski|Kerstin Kunz,,38939691,https://www.aclweb.org/anthology/2020.codi-1.6 -WS-16,WS-16.14,Contextualized Embeddings for Connective Disambiguation in Shallow Discourse Parsing,René Knaebel|Manfred Stede,,38939692,https://www.aclweb.org/anthology/2020.codi-1.7 -WS-16,WS-16.15,DSNDM: Deep Siamese Neural Discourse Model with Attention for Text Pairs Categorization and Ranking,Alexander Chernyavskiy|Dmitry Ilvovsky,,38939693,https://www.aclweb.org/anthology/2020.codi-1.8 -WS-16,WS-16.17,Do sentence embeddings capture discourse properties of sentences from Scientific Abstracts ?,Laurine Huber|Chaker Memmadi|Mathilde Dargnat|Yannick Toussaint,,38939694,https://www.aclweb.org/anthology/2020.codi-1.9 +WS-15,WS-15.929,ESTeR: Combining Word Co-occurrences and Word Associations for Unsupervised Emotion Detection,Polina Rozenshtein,"Accurate detection of emotions in user- generated text was shown to have several applications for e-commerce, public well-being, and disaster management. Currently, the state-of-the-art performance for emotion detection in text is obtained using complex, deep learning models trained on domain-specific, labeled data. In this paper, we propose ESTeR , an unsupervised model for identifying emotions using a novel similarity function based on random walks on graphs. Our model combines large-scale word co-occurrence information with word-associations from lexicons avoiding not only the dependence on labeled datasets, but also an explicit mapping of words to latent spaces used in emotion-enriched word embeddings. Our similarity function can also be computed efficiently. We study a range of datasets including recent tweets related to COVID-19 to illustrate the superior performance of our model and report insights on public emotions during the on-going pandemic.",38940135,https://www.aclweb.org/anthology/2020.findings-emnlp.93 +WS-16,WS-16.10,Exploring Span Representations in Neural Coreference Resolution,Patrick Kahardipraja|Olena Vyshnevska|Sharid Loáiciga,"In coreference resolution, span representations play a key role to predict coreference links accurately. We present a thorough examination of the span representation derived by applying BERT on coreference resolution (Joshi et al., 2019) using a probing model. Our results show that the span representation is able to encode a significant amount of coreference information. In addition, we find that the head-finding attention mechanism involved in creating the spans is crucial in encoding coreference knowledge. Last, our analysis shows that the span representation cannot capture non-local coreference as efficiently as local coreference.",38939689,https://www.aclweb.org/anthology/2020.codi-1.4 +WS-16,WS-16.11,Supporting Comedy Writers: Predicting Audience's Response from Sketch Comedy and Crosstalk Scripts,Maolin Li,"Sketch comedy and crosstalk are two popular types of comedy. They can relieve people’s stress and thus benefit their mental health, especially when performances and scripts are high-quality. However, writing a script is time-consuming and its quality is difficult to achieve. In order to minimise the time and effort needed for producing an excellent script, we explore ways of predicting the audience’s response from the comedy scripts. For this task, we present a corpus of annotated scripts from popular television entertainment programmes in recent years. Annotations include a) text classification labels, indicating which actor’s lines made the studio audience laugh; b) information extraction labels, i.e. the text spans that made the audience laughed immediately after the performers said them. The corpus will also be useful for dialogue systems and discourse analysis, since our annotations are based on entire scripts. In addition, we evaluate different baseline algorithms. Experimental results demonstrate that BERT models can achieve the best predictions among all the baseline methods. Furthermore, we conduct an error analysis and investigate predictions across scripts with different styles.",38939690,https://www.aclweb.org/anthology/2020.codi-1.5 +WS-16,WS-16.13,Exploring Coreference Features in Heterogeneous Data with Text Classification,Ekaterina Lapshinova-Koltunski|Kerstin Kunz,"The present paper focuses on variation phenomena in coreference chains. We address the hypothesis that the degree of structural variation between chain elements depends on language-specific constraints and preferences and, even more, on the communicative situation of language production. We define coreference features that also include reference to abstract entities and events. These features are inspired through several sources – cognitive parameters, pragmatic factors and typological status. We pay attention to the distributions of these features in a dataset containing English and German texts of spoken and written discourse mode, which can be classified into seven different registers. We apply text classification and feature selection to find out how these variational dimensions (language, mode and register) impact on coreference features. Knowledge on the variation under analysis is valuable for contrastive linguistics, translation studies and multilingual natural language processing (NLP), e.g. machine translation or cross-lingual coreference resolution.",38939691,https://www.aclweb.org/anthology/2020.codi-1.6 +WS-16,WS-16.14,Contextualized Embeddings for Connective Disambiguation in Shallow Discourse Parsing,René Knaebel|Manfred Stede,"This paper studies a novel model that simplifies the disambiguation of connectives for explicit discourse relations. We use a neural approach that integrates contextualized word embeddings and predicts whether a connective candidate is part of a discourse relation or not. We study the influence of those context-specific embeddings. Further, we show the benefit of training the tasks of connective disambiguation and sense classification together at the same time. The success of our approach is supported by state-of-the-art results.",38939692,https://www.aclweb.org/anthology/2020.codi-1.7 +WS-16,WS-16.15,DSNDM: Deep Siamese Neural Discourse Model with Attention for Text Pairs Categorization and Ranking,Alexander Chernyavskiy|Dmitry Ilvovsky,"In this paper, the utility and advantages of the discourse analysis for text pairs categorization and ranking are investigated. We consider two tasks in which discourse structure seems useful and important: automatic verification of political statements, and ranking in question answering systems. We propose a neural network based approach to learn the match between pairs of discourse tree structures. To this end, the neural TreeLSTM model is modified to effectively encode discourse trees and DSNDM model based on it is suggested to analyze pairs of texts. In addition, the integration of the attention mechanism in the model is proposed. Moreover, different ranking approaches are investigated for the second task. In the paper, the comparison with state-of-the-art methods is given. Experiments illustrate that combination of neural networks and discourse structure in DSNDM is effective since it reaches top results in the assigned tasks. The evaluation also demonstrates that discourse analysis improves quality for the processing of longer texts.",38939693,https://www.aclweb.org/anthology/2020.codi-1.8 +WS-16,WS-16.17,Do sentence embeddings capture discourse properties of sentences from Scientific Abstracts ?,Laurine Huber|Chaker Memmadi|Mathilde Dargnat|Yannick Toussaint,"We introduce four tasks designed to determine which sentence encoders best capture discourse properties of sentences from scientific abstracts, namely coherence and cohesion between clauses of a sentence, and discourse relations within sentences. We show that even if contextual encoders such as BERT or SciBERT encodes the coherence in discourse units, they do not help to predict three discourse relations commonly used in scientific abstracts. We discuss what these results underline, namely that these discourse relations are based on particular phrasing that allow non-contextual encoders to perform well.",38939694,https://www.aclweb.org/anthology/2020.codi-1.9 WS-16,WS-16.18,Unsupervised Inference of Data-Driven Discourse Structures using a Tree Auto-Encoder,Patrick Huber|Giuseppe Carenini,,38939695, WS-16,WS-16.19,Large Discourse Treebanks from Scalable Distant Supervision,Patrick Huber|Giuseppe Carenini,,38939696, -WS-16,WS-16.1951,Adapting Coreference Resolution to Twitter Conversations,Berfin Aktaş,,38940697,https://www.aclweb.org/anthology/2020.findings-emnlp.222 +WS-16,WS-16.1951,Adapting Coreference Resolution to Twitter Conversations,Berfin Aktaş,"The performance of standard coreference resolution is known to drop significantly on Twitter texts. We improve the performance of the (Lee et al., 2018) system, which is originally trained on OntoNotes, by retraining on manually-annotated Twitter conversation data. Further experiments by combining different portions of OntoNotes with Twitter data show that selecting text genres for the training data can beat the mere maximization of training data amount. In addition, we inspect several phenomena such as the role of deictic pronouns in conversational data, and present additional results for variant settings. Our best configuration improves the performance of the”out of the box” system by 21.6%.",38940697,https://www.aclweb.org/anthology/2020.findings-emnlp.222 WS-16,WS-16.20,"Discourse for Argument Mining, and Argument Mining as Discourse",Diane Litman,,38939697, -WS-16,WS-16.208,Converting the Point of View of Messages Spoken to Virtual Assistants,Isabelle G. Lee,,38940694,https://www.aclweb.org/anthology/2020.findings-emnlp.15 -WS-16,WS-16.21,Joint Modeling of Arguments for Event Understanding,Yunmo Chen|Tongfei Chen|Benjamin Van Durme,,38939698,https://www.aclweb.org/anthology/2020.codi-1.10 -WS-16,WS-16.2195,Social Commonsense Reasoning with Multi-Head Knowledge Attention,Debjit Paul,,38940698,https://www.aclweb.org/anthology/2020.findings-emnlp.267 -WS-16,WS-16.22,Analyzing Neural Discourse Coherence Models,Youmna Farag|Josef Valvoda|Helen Yannakoudakis|Ted Briscoe,,38939699,https://www.aclweb.org/anthology/2020.codi-1.11 -WS-16,WS-16.23,Computational Interpretation of Recency for the Choice of Referring Expressions in Discourse,Fahime Same|Kees Van Deemter,,38939700,https://www.aclweb.org/anthology/2020.codi-1.12 -WS-16,WS-16.24,Do We Really Need That Many Parameters In Transformer For Extractive Summarization? Discourse Can Help !,Wen Xiao|Patrick Huber|Giuseppe Carenini,,38939701,https://www.aclweb.org/anthology/2020.codi-1.13 -WS-16,WS-16.2487-WS16,Composed Variational Natural Language Generation for Few-shot Intents,Congying Xia,,38940699,https://www.aclweb.org/anthology/2020.findings-emnlp.303 -WS-16,WS-16.26,Extending Implicit Discourse Relation Recognition to the PDTB-3,Li Liang|Zheng Zhao|Bonnie Webber,,38939702,https://www.aclweb.org/anthology/2020.codi-1.14 -WS-16,WS-16.263,A Hierarchical Network for Abstractive Meeting Summarization with Cross-Domain Pretraining,Chenguang Zhu,,38940695,https://www.aclweb.org/anthology/2020.findings-emnlp.19 -WS-16,WS-16.27,"TED-MDB Lexicons: TrEnConnLex, PtEnConnLex",Murathan Kurfalı|Sibel Ozer|Deniz Zeyrek|Amália Mendes,,38939703,https://www.aclweb.org/anthology/2020.codi-1.15 -WS-16,WS-16.28,Eliminating Mention Overlaps: Evaluation of Coreference Resolution Systems Under Adversarial Attacks,Haixia Chai|Wei Zhao|Steffen Eger|Michael Strube,,38939704,https://www.aclweb.org/anthology/2020.codi-1.16 +WS-16,WS-16.208,Converting the Point of View of Messages Spoken to Virtual Assistants,Isabelle G. Lee,"Virtual Assistants can be quite literal at times. If the user says “tell Bob I love him,” most virtual assistants will extract the message “I love him” and send it to the user’s contact named Bob, rather than properly converting the message to “I love you.” We designed a system to allow virtual assistants to take a voice message from one user, convert the point of view of the message, and then deliver the result to its target user. We developed a rule-based model, which integrates a linear text classification model, part-of-speech tagging, and constituency parsing with rule-based transformation methods. We also investigated Neural Machine Translation (NMT) approaches, including LSTMs, CopyNet, and T5. We explored 5 metrics to gauge both naturalness and faithfulness automatically, and we chose to use BLEU plus METEOR for faithfulness and relative perplexity using a separately trained language model (GPT) for naturalness. Transformer-Copynet and T5 performed similarly on faithfulness metrics, with T5 achieving slight edge, a BLEU score of 63.8 and a METEOR score of 83.0. CopyNet was the most natural, with a relative perplexity of 1.59. CopyNet also has 37 times fewer parameters than T5. We have publicly released our dataset, which is composed of 46,565 crowd-sourced samples.",38940694,https://www.aclweb.org/anthology/2020.findings-emnlp.15 +WS-16,WS-16.21,Joint Modeling of Arguments for Event Understanding,Yunmo Chen|Tongfei Chen|Benjamin Van Durme,"We recognize the task of event argument linking in documents as similar to that of intent slot resolution in dialogue, providing a Transformer-based model that extends from a recently proposed solution to resolve references to slots. The approach allows for joint consideration of argument candidates given a detected event, which we illustrate leads to state-of-the-art performance in multi-sentence argument linking.",38939698,https://www.aclweb.org/anthology/2020.codi-1.10 +WS-16,WS-16.2195,Social Commonsense Reasoning with Multi-Head Knowledge Attention,Debjit Paul,"Social Commonsense Reasoning requires understanding of text, knowledge about social events and their pragmatic implications, as well as commonsense reasoning skills. In this work we propose a novel multi-head knowledge attention model that encodes semi-structured commonsense inference rules and learns to incorporate them in a transformer-based reasoning cell.We assess the model’s performance on two tasks that require different reasoning skills: Abductive Natural Language Inference and Counterfactual Invariance Prediction as a new task. We show that our proposed model improves performance over strong state-of-the-art models (i.e., RoBERTa) across both reasoning tasks. Notably we are, to the best of our knowledge, the first to demonstrate that a model that learns to perform counterfactual reasoning helps predicting the best explanation in an abductive reasoning task. We validate the robustness of the model’s reasoning capabilities by perturbing the knowledge and provide qualitative analysis on the model’s knowledge incorporation capabilities.",38940698,https://www.aclweb.org/anthology/2020.findings-emnlp.267 +WS-16,WS-16.22,Analyzing Neural Discourse Coherence Models,Youmna Farag|Josef Valvoda|Helen Yannakoudakis|Ted Briscoe,"In this work, we systematically investigate how well current models of coherence can capture aspects of text implicated in discourse organisation. We devise two datasets of various linguistic alterations that undermine coherence and test model sensitivity to changes in syntax and semantics. We furthermore probe discourse embedding space and examine the knowledge that is encoded in representations of coherence. We hope this study shall provide further insight into how to frame the task and improve models of coherence assessment further. Finally, we make our datasets publicly available as a resource for researchers to use to test discourse coherence models.",38939699,https://www.aclweb.org/anthology/2020.codi-1.11 +WS-16,WS-16.23,Computational Interpretation of Recency for the Choice of Referring Expressions in Discourse,Fahime Same|Kees Van Deemter,"First, we discuss the most common linguistic perspectives on the concept of recency and propose a taxonomy of recency metrics employed in Machine Learning studies for choosing the form of referring expressions in discourse context. We then report on a Multi-Layer Perceptron study and a Sequential Forward Search experiment, followed by Bayes Factor analysis of the outcomes. The results suggest that recency metrics counting paragraphs and sentences contribute to referential choice prediction more than other recency-related metrics. Based on the results of our analysis, we argue that, sensitivity to discourse structure is important for recency metrics used in determining referring expression forms.",38939700,https://www.aclweb.org/anthology/2020.codi-1.12 +WS-16,WS-16.24,Do We Really Need That Many Parameters In Transformer For Extractive Summarization? Discourse Can Help !,Wen Xiao|Patrick Huber|Giuseppe Carenini,"The multi-head self-attention of popular transformer models is widely used within Natural Language Processing (NLP), including for the task of extractive summarization. With the goal of analyzing and pruning the parameter-heavy self-attention mechanism, there are multiple approaches proposing more parameter-light self-attention alternatives. In this paper, we present a novel parameter-lean self-attention mechanism using discourse priors. Our new tree self-attention is based on document-level discourse information, extending the recently proposed “Synthesizer” framework with another lightweight alternative. We show empirical results that our tree self-attention approach achieves competitive ROUGE-scores on the task of extractive summarization. When compared to the original single-head transformer model, the tree attention approach reaches similar performance on both, EDU and sentence level, despite the significant reduction of parameters in the attention component. We further significantly outperform the 8-head transformer model on sentence level when applying a more balanced hyper-parameter setting, requiring an order of magnitude less parameters.",38939701,https://www.aclweb.org/anthology/2020.codi-1.13 +WS-16,WS-16.2487-WS16,Composed Variational Natural Language Generation for Few-shot Intents,Congying Xia,"In this paper, we focus on generating training examples for few-shot intents in the realistic imbalanced scenario. To build connections between existing many-shot intents and few-shot intents, we consider an intent as a combination of a domain and an action, and propose a composed variational natural language generator (CLANG), a transformer-based conditional variational autoencoder. CLANG utilizes two latent variables to represent the utterances corresponding to two different independent parts (domain and action) in the intent, and the latent variables are composed together to generate natural examples. Additionally, to improve the generator learning, we adopt the contrastive regularization loss that contrasts the in-class with the out-of-class utterance generation given the intent. To evaluate the quality of the generated utterances, experiments are conducted on the generalized few-shot intent detection task. Empirical results show that our proposed model achieves state-of-the-art performances on two real-world intent detection datasets.",38940699,https://www.aclweb.org/anthology/2020.findings-emnlp.303 +WS-16,WS-16.26,Extending Implicit Discourse Relation Recognition to the PDTB-3,Li Liang|Zheng Zhao|Bonnie Webber,"The PDTB-3 contains many more Implicit discourse relations than the previous PDTB-2. This is in part because implicit relations have now been annotated within sentences as well as between them. In addition, some now co-occur with explicit discourse relations, instead of standing on their own. Here we show that while this can complicate the problem of identifying the location of implicit discourse relations, it can in turn simplify the problem of identifying their senses. We present data to support this claim, as well as methods that can serve as a non-trivial baseline for future state-of-the-art recognizers for implicit discourse relations.",38939702,https://www.aclweb.org/anthology/2020.codi-1.14 +WS-16,WS-16.263,A Hierarchical Network for Abstractive Meeting Summarization with Cross-Domain Pretraining,Chenguang Zhu,"With the abundance of automatic meeting transcripts, meeting summarization is of great interest to both participants and other parties. Traditional methods of summarizing meetings depend on complex multi-step pipelines that make joint optimization intractable. Meanwhile, there are a handful of deep neural models for text summarization and dialogue systems. However, the semantic structure and styles of meeting transcripts are quite different from articles and conversations. In this paper, we propose a novel abstractive summary network that adapts to the meeting scenario. We design a hierarchical structure to accommodate long meeting transcripts and a role vector to depict the difference among speakers. Furthermore, due to the inadequacy of meeting summary data, we pretrain the model on large-scale news summary data. Empirical results show that our model outperforms previous approaches in both automatic metrics and human evaluation. For example, on ICSI dataset, the ROUGE-1 score increases from 34.66% to 46.28%.",38940695,https://www.aclweb.org/anthology/2020.findings-emnlp.19 +WS-16,WS-16.27,"TED-MDB Lexicons: TrEnConnLex, PtEnConnLex",Murathan Kurfalı|Sibel Ozer|Deniz Zeyrek|Amália Mendes,"In this work, we present two new bilingual discourse connective lexicons, namely, for Turkish-English and European Portuguese-English created automatically using the existing discourse relation-aligned TED-MDB corpus. In their current form, the Pt-En lexicon includes 95 entries, whereas the Tr-En lexicon contains 133 entries. The lexicons constitute the first step of a larger project of developing a multilingual discourse connective lexicon.",38939703,https://www.aclweb.org/anthology/2020.codi-1.15 +WS-16,WS-16.28,Eliminating Mention Overlaps: Evaluation of Coreference Resolution Systems Under Adversarial Attacks,Haixia Chai|Wei Zhao|Steffen Eger|Michael Strube,"A substantial overlap of coreferent mentions in the CoNLL dataset magnifies the recent progress on coreference resolution. This is because the CoNLL benchmark fails to evaluate the ability of coreference resolvers that requires linking novel mentions unseen at train time. In this work, we create a new dataset based on CoNLL, which largely decreases mention overlaps in the entire dataset and exposes the limitations of published resolvers on two aspects—lexical inference ability and understanding of low-level orthographic noise. Our findings show (1) the requirements for embeddings, used in resolvers, and for coreference resolutions are, by design, in conflict and (2) adversarial approaches are sometimes not legitimate to mitigate the obstacles, as they may falsely introduce mention overlaps in adversarial training and test sets, thus giving an inflated impression for the improvements.",38939704,https://www.aclweb.org/anthology/2020.codi-1.16 WS-16,WS-16.29,Exploring aspects of similarity between spoken personal narratives by disentangling them into narrative clause types,Belen Saldias|Deb Roy,,38939705, -WS-16,WS-16.31,Coreference for Discourse Parsing: A Neural Approach,Grigorii Guz|Giuseppe Carenini,,38939706,https://www.aclweb.org/anthology/2020.codi-1.17 +WS-16,WS-16.31,Coreference for Discourse Parsing: A Neural Approach,Grigorii Guz|Giuseppe Carenini,"We present preliminary results on investigating the benefits of coreference resolution features for neural RST discourse parsing by considering different levels of coupling of the discourse parser with the coreference resolver. In particular, starting with a strong baseline neural parser unaware of any coreference information, we compare a parser which utilizes only the output of a neural coreference resolver, with a more sophisticated model, where discourse parsing and coreference resolution are jointly learned in a neural multitask fashion. Results indicate that these initial attempts to incorporate coreference information do not boost the performance of discourse parsing in a statistically significant way.",38939706,https://www.aclweb.org/anthology/2020.codi-1.17 WS-16,WS-16.3452,Thinking Like a Skeptic: Defeasible Inference in Natural Language,Rachel Rudinger,"Defeasible inference is a mode of reasoning in which an inference (X is a bird, therefore X flies) may be weakened or overturned in light of new evidence (X is a penguin). Though long recognized in classical AI and philosophy, defeasible inference has not been extensively studied in the context of contemporary data-driven research on natural language inference and commonsense reasoning. We introduce Defeasible NLI (abbreviated \delta-NLI), a dataset for defeasible inference in natural language. Defeasible NLI contains extensions to three existing inference datasets covering diverse modes of reasoning: common sense, natural language inference, and social norms. From Defeasible NLI, we develop both a classification and generation task for defeasible inference, and demonstrate that the generation task is much more challenging. Despite lagging human performance, however, generative models trained on this data are capable of writing sentences that weaken or strengthen a specified inference up to 68% of the time.",38940700,https://www.aclweb.org/anthology/2020.findings-emnlp.418 -WS-16,WS-16.3598,Paraphrasing vs Coreferring: Two Sides of the Same Coin,Yehudit Meged,,38940701,https://www.aclweb.org/anthology/2020.findings-emnlp.440 -WS-16,WS-16.4,How does discourse affect Spanish-Chinese Translation? A case study based on a Spanish-Chinese parallel corpus,Shuyuan Cao,,38939686,https://www.aclweb.org/anthology/2020.codi-1.1 -WS-16,WS-16.475,Dialogue Generation on Infrequent Sentence Functions via Structured Meta-Learning,Yifan Gao,,38940696,https://www.aclweb.org/anthology/2020.findings-emnlp.40 +WS-16,WS-16.3598,Paraphrasing vs Coreferring: Two Sides of the Same Coin,Yehudit Meged,"We study the potential synergy between two different NLP tasks, both confronting predicate lexical variability: identifying predicate paraphrases, and event coreference resolution. First, we used annotations from an event coreference dataset as distant supervision to re-score heuristically-extracted predicate paraphrases. The new scoring gained more than 18 points in average precision upon their ranking by the original scoring method. Then, we used the same re-ranking features as additional inputs to a state-of-the-art event coreference resolution model, which yielded modest but consistent improvements to the model’s performance. The results suggest a promising direction to leverage data and models for each of the tasks to the benefit of the other.",38940701,https://www.aclweb.org/anthology/2020.findings-emnlp.440 +WS-16,WS-16.4,How does discourse affect Spanish-Chinese Translation? A case study based on a Spanish-Chinese parallel corpus,Shuyuan Cao,"With their huge speaking populations in the world, Spanish and Chinese occupy important positions in linguistic studies. Since the two languages come from different language systems, the translation between Spanish and Chinese is complicated. A comparative study for the language pair can discover the discourse differences between Spanish and Chinese, and can benefit the Spanish-Chinese translation. In this work, based on a Spanish-Chinese parallel corpus annotated with discourse information, we compare the annotation results between the language pair and analyze how discourse affects Spanish-Chinese translation. The research results in our study can help human translators who work with the language pair.",38939686,https://www.aclweb.org/anthology/2020.codi-1.1 +WS-16,WS-16.475,Dialogue Generation on Infrequent Sentence Functions via Structured Meta-Learning,Yifan Gao,"Sentence function is an important linguistic feature indicating the communicative purpose in uttering a sentence. Incorporating sentence functions into conversations has shown improvements in the quality of generated responses. However, the number of utterances for different types of fine-grained sentence functions is extremely imbalanced. Besides a small number of high-resource sentence functions, a large portion of sentence functions is infrequent. Consequently, dialogue generation conditioned on these infrequent sentence functions suffers from data deficiency. In this paper, we investigate a structured meta-learning (SML) approach for dialogue generation on infrequent sentence functions. We treat dialogue generation conditioned on different sentence functions as separate tasks, and apply model-agnostic meta-learning to high-resource sentence functions data. Furthermore, SML enhances meta-learning effectiveness by promoting knowledge customization among different sentence functions but simultaneously preserving knowledge generalization for similar sentence functions. Experimental results demonstrate that SML not only improves the informativeness and relevance of generated responses, but also can generate responses consistent with the target sentence functions. Code will be public to facilitate the research along this line.",38940696,https://www.aclweb.org/anthology/2020.findings-emnlp.40 WS-16,WS-16.6,Free the Plural: Unrestricted Split-Antecedent Anaphora Resolution,Juntao Yu|Nafise Sadat Moosavi|Silviu Paun|Massimo Poesio,,38940702, WS-16,WS-16.7,Multitask Learning-Based Neural Bridging Reference Resolution,Juntao Yu|Massimo Poesio,,38940703, -WS-16,WS-16.8,Beyond Adjacency Pairs: Extracting Longer Regularities in Human-Machine Dialogues,Maitreyee Maitreyee,,38939687,https://www.aclweb.org/anthology/2020.codi-1.2 -WS-16,WS-16.9,Using Type Information to Improve Entity Coreference Resolution,Sopan Khosla|Carolyn Rose,,38939688,https://www.aclweb.org/anthology/2020.codi-1.3 -WS-17,WS-17.10,A Novel Methodology for Developing Automatic Harassment Classifiers for Twitter,Ishaan Arora|Julia Guo|Sarah Ita Levitan|Susan McGregor|Julia Hirschberg,,38939517,https://www.aclweb.org/anthology/2020.alw-1.2 -WS-17,WS-17.13,Countering hate on social media: Large scale classification of hate and counter speech,Joshua Garland|Keyan Ghazi-Zahedi|Jean-Gabriel Young|Laurent Hébert-Dufresne|Mirta Galesic,,38939518,https://www.aclweb.org/anthology/2020.alw-1.13 -WS-17,WS-17.15,Impact of politically biased data on hate speech classification,Maximilian Wich|Jan Bauer|Georg Groh,,38939519,https://www.aclweb.org/anthology/2020.alw-1.7 -WS-17,WS-17.16,A Unified Taxonomy of Harmful Content,Michele Banko|Brendon MacKeen|Laurie Ray,,38939520,https://www.aclweb.org/anthology/2020.alw-1.16 -WS-17,WS-17.19,In Data We Trust: A Critical Analysis of Hate Speech Detection Datasets,Kosisochukwu Madukwe|Xiaoying Gao|Bing Xue,,38939521,https://www.aclweb.org/anthology/2020.alw-1.18 -WS-17,WS-17.2,Moderating Our (Dis)Content: Renewing the Regulatory Approach,Claire Pershan,,38939516,https://www.aclweb.org/anthology/2020.alw-1.14 -WS-17,WS-17.2217,Findings: A little goes a long way: Improving toxic language classification despite data scarcity,Mika Juuti|Tommi Gröndahl|Adrian Flanagan|N. Asokan,,38941227,https://www.aclweb.org/anthology/2020.findings-emnlp.269 -WS-17,WS-17.24,Fine-tuning BERT for multi-domain and multi-label incivil language detection,Kadir Bulut Ozler|Kate Kenski|Steve Rains|Yotam Shmargad|Kevin Coe|Steven Bethard,,38939522,https://www.aclweb.org/anthology/2020.alw-1.4 -WS-17,WS-17.25,Six Attributes of Unhealthy Conversations,Ilan Price|Jordan Gifford-Moore|Jory Flemming|Saul Musker|Maayan Roichman|Guillaume Sylvain|Nithum Thain|Lucas Dixon|Jeffrey Sorensen,,38939523,https://www.aclweb.org/anthology/2020.alw-1.15 -WS-17,WS-17.31,Reducing Unintended Identity Bias in Russian Hate Speech Detection,Nadezhda Zueva|Madina Kabirova|Pavel Kalaidin,,38939524,https://www.aclweb.org/anthology/2020.alw-1.8 +WS-16,WS-16.8,Beyond Adjacency Pairs: Extracting Longer Regularities in Human-Machine Dialogues,Maitreyee Maitreyee,"This work proposes a framework to predict sequences in dialogues, using turn based syntactic features and dialogue control functions. Syntactic features were extracted using dependency parsing, while dialogue control functions were manually labelled. These features were transformed using tf-idf and word embedding; feature selection was done using Principal Component Analysis (PCA). We ran experiments on six combinations of features to predict sequences with Hierarchical Agglomerative Clustering. An analysis of the clustering results indicate that using word embeddings and syntactic features, significantly improved the results.",38939687,https://www.aclweb.org/anthology/2020.codi-1.2 +WS-16,WS-16.9,Using Type Information to Improve Entity Coreference Resolution,Sopan Khosla|Carolyn Rose,"Coreference resolution (CR) is an essential part of discourse analysis. Most recently, neural approaches have been proposed to improve over SOTA models from earlier paradigms. So far none of the published neural models leverage external semantic knowledge such as type information. This paper offers the first such model and evaluation, demonstrating modest gains in accuracy by introducing either gold standard or predicted types. In the proposed approach, type information serves both to (1) improve mention representation and (2) create a soft type consistency check between coreference candidate mentions. Our evaluation covers two different grain sizes of types over four different benchmark corpora.",38939688,https://www.aclweb.org/anthology/2020.codi-1.3 +WS-17,WS-17.10,A Novel Methodology for Developing Automatic Harassment Classifiers for Twitter,Ishaan Arora|Julia Guo|Sarah Ita Levitan|Susan McGregor|Julia Hirschberg,"Most efforts at identifying abusive speech online rely on public corpora that have been scraped from websites using keyword-based queries or released by site or platform owners for research purposes. These are typically labeled by crowd-sourced annotators – not the targets of the abuse themselves. While this method of data collection supports fast development of machine learning classifiers, the models built on them often fail in the context of real-world harassment and abuse, which contain nuances less easily identified by non-targets. Here, we present a mixed-methods approach to create classifiers for abuse and harassment which leverages direct engagement with the target group in order to achieve high quality and ecological validity of data sets and labels, and to generate deeper insights into the key tactics of bad actors. We use women journalists’ experience on Twitter as an initial community of focus. We identify several structural mechanisms of abuse that we believe will generalize to other target communities.",38939517,https://www.aclweb.org/anthology/2020.alw-1.2 +WS-17,WS-17.13,Countering hate on social media: Large scale classification of hate and counter speech,Joshua Garland|Keyan Ghazi-Zahedi|Jean-Gabriel Young|Laurent Hébert-Dufresne|Mirta Galesic,"Hateful rhetoric is plaguing online discourse, fostering extreme societal movements and possibly giving rise to real-world violence. A potential solution to this growing global problem is citizen-generated counter speech where citizens actively engage with hate speech to restore civil non-polarized discourse. However, its actual effectiveness in curbing the spread of hatred is unknown and hard to quantify. One major obstacle to researching this question is a lack of large labeled data sets for training automated classifiers to identify counter speech. Here we use a unique situation in Germany where self-labeling groups engaged in organized online hate and counter speech. We use an ensemble learning algorithm which pairs a variety of paragraph embeddings with regularized logistic regression functions to classify both hate and counter speech in a corpus of millions of relevant tweets from these two groups. Our pipeline achieves macro F1 scores on out of sample balanced test sets ranging from 0.76 to 0.97—accuracy in line and even exceeding the state of the art. We then use the classifier to discover hate and counter speech in more than 135,000 fully-resolved Twitter conversations occurring from 2013 to 2018 and study their frequency and interaction. Altogether, our results highlight the potential of automated methods to evaluate the impact of coordinated counter speech in stabilizing conversations on social media.",38939518,https://www.aclweb.org/anthology/2020.alw-1.13 +WS-17,WS-17.15,Impact of politically biased data on hate speech classification,Maximilian Wich|Jan Bauer|Georg Groh,"One challenge that social media platforms are facing nowadays is hate speech. Hence, automatic hate speech detection has been increasingly researched in recent years - in particular with the rise of deep learning. A problem of these models is their vulnerability to undesirable bias in training data. We investigate the impact of political bias on hate speech classification by constructing three politically-biased data sets (left-wing, right-wing, politically neutral) and compare the performance of classifiers trained on them. We show that (1) political bias negatively impairs the performance of hate speech classifiers and (2) an explainable machine learning model can help to visualize such bias within the training data. The results show that political bias in training data has an impact on hate speech classification and can become a serious issue.",38939519,https://www.aclweb.org/anthology/2020.alw-1.7 +WS-17,WS-17.16,A Unified Taxonomy of Harmful Content,Michele Banko|Brendon MacKeen|Laurie Ray,"The ability to recognize harmful content within online communities has come into focus for researchers, engineers and policy makers seeking to protect users from abuse. While the number of datasets aiming to capture forms of abuse has grown in recent years, the community has not standardized around how various harmful behaviors are defined, creating challenges for reliable moderation, modeling and evaluation. As a step towards attaining shared understanding of how online abuse may be modeled, we synthesize the most common types of abuse described by industry, policy, community and health experts into a unified typology of harmful content, with detailed criteria and exceptions for each type of abuse.",38939520,https://www.aclweb.org/anthology/2020.alw-1.16 +WS-17,WS-17.19,In Data We Trust: A Critical Analysis of Hate Speech Detection Datasets,Kosisochukwu Madukwe|Xiaoying Gao|Bing Xue,"Recently, a few studies have discussed the limitations of datasets collected for the task of detecting hate speech from different viewpoints. We intend to contribute to the conversation by providing a consolidated overview of these issues pertaining to the data that debilitate research in this area. Specifically, we discuss how the varying pre-processing steps and the format for making data publicly available result in highly varying datasets that make an objective comparison between studies difficult and unfair. There is currently no study (to the best of our knowledge) focused on comparing the attributes of existing datasets for hate speech detection, outlining their limitations and recommending approaches for future research. This work intends to fill that gap and become the one-stop shop for information regarding hate speech datasets.",38939521,https://www.aclweb.org/anthology/2020.alw-1.18 +WS-17,WS-17.2,Moderating Our (Dis)Content: Renewing the Regulatory Approach,Claire Pershan,"As online platforms become central to our democracies, the problem of toxic content threatens the free flow of information and the enjoyment of fundamental rights. But effective policy response to toxic content must grasp the idiosyncrasies and interconnectedness of content moderation across a fragmented online landscape. This report urges regulators and legislators to consider a range of platforms and moderation approaches in the regulation. In particular, it calls for a holistic, process-oriented regulatory approach that accounts for actors beyond the handful of dominant platforms that currently shape public debate.",38939516,https://www.aclweb.org/anthology/2020.alw-1.14 +WS-17,WS-17.2217,Findings: A little goes a long way: Improving toxic language classification despite data scarcity,Mika Juuti|Tommi Gröndahl|Adrian Flanagan|N. Asokan,"Detection of some types of toxic language is hampered by extreme scarcity of labeled training data. Data augmentation – generating new synthetic data from a labeled seed dataset – can help. The efficacy of data augmentation on toxic language classification has not been fully explored. We present the first systematic study on how data augmentation techniques impact performance across toxic language classifiers, ranging from shallow logistic regression architectures to BERT – a state-of-the-art pretrained Transformer network. We compare the performance of eight techniques on very scarce seed datasets. We show that while BERT performed the best, shallow classifiers performed comparably when trained on data augmented with a combination of three techniques, including GPT-2-generated sentences. We discuss the interplay of performance and computational overhead, which can inform the choice of techniques under different constraints.",38941227,https://www.aclweb.org/anthology/2020.findings-emnlp.269 +WS-17,WS-17.24,Fine-tuning BERT for multi-domain and multi-label incivil language detection,Kadir Bulut Ozler|Kate Kenski|Steve Rains|Yotam Shmargad|Kevin Coe|Steven Bethard,"Incivility is a problem on social media, and it comes in many forms (name-calling, vulgarity, threats, etc.) and domains (microblog posts, online news comments, Wikipedia edits, etc.). Training machine learning models to detect such incivility must handle the multi-label and multi-domain nature of the problem. We present a BERT-based model for incivility detection and propose several approaches for training it for multi-label and multi-domain datasets. We find that individual binary classifiers outperform a joint multi-label classifier, and that simply combining multiple domains of training data outperforms other recently-proposed fine tuning strategies. We also establish new state-of-the-art performance on several incivility detection datasets.",38939522,https://www.aclweb.org/anthology/2020.alw-1.4 +WS-17,WS-17.25,Six Attributes of Unhealthy Conversations,Ilan Price|Jordan Gifford-Moore|Jory Flemming|Saul Musker|Maayan Roichman|Guillaume Sylvain|Nithum Thain|Lucas Dixon|Jeffrey Sorensen,"We present a new dataset of approximately 44000 comments labeled by crowdworkers. Each comment is labelled as either ‘healthy’ or ‘unhealthy’, in addition to binary labels for the presence of six potentially ‘unhealthy’ sub-attributes: (1) hostile; (2) antagonistic, insulting, provocative or trolling; (3) dismissive; (4) condescending or patronising; (5) sarcastic; and/or (6) an unfair generalisation. Each label also has an associated confidence score. We argue that there is a need for datasets which enable research based on a broad notion of ‘unhealthy online conversation’. We build this typology to encompass a substantial proportion of the individual comments which contribute to unhealthy online conversation. For some of these attributes, this is the first publicly available dataset of this scale. We explore the quality of the dataset, present some summary statistics and initial models to illustrate the utility of this data, and highlight limitations and directions for further research.",38939523,https://www.aclweb.org/anthology/2020.alw-1.15 +WS-17,WS-17.31,Reducing Unintended Identity Bias in Russian Hate Speech Detection,Nadezhda Zueva|Madina Kabirova|Pavel Kalaidin,"Toxicity has become a grave problem for many online communities, and has been growing across many languages, including Russian. Hate speech creates an environment of intimidation, discrimination, and may even incite some real-world violence. Both researchers and social platforms have been focused on developing models to detect toxicity in online communication for a while now. A common problem of these models is the presence of bias towards some words (e.g. woman, black, jew or женщина, черный, еврей) that are not toxic, but serve as triggers for the classifier due to model caveats. In this paper, we describe our efforts towards classifying hate speech in Russian, and propose simple techniques of reducing unintended bias, such as generating training data with language models using terms and words related to protected identities as context and applying word dropout to such words.",38939524,https://www.aclweb.org/anthology/2020.alw-1.8 WS-17,WS-17.32,Investigating takedowns of abuse on Twitter,Rosalie Gillett|Nicolas Suzor|Jean Burgess|Bridget Harris|Molly Dragiewicz,,38939525, -WS-17,WS-17.37,Detecting East Asian Prejudice on Social Media,Bertie Vidgen|Scott Hale|Ella Guest|Helen Margetts|David Broniatowski|Zeerak Waseem|Austin Botelho|Matthew Hall|Rebekah Tromble,,38939526,https://www.aclweb.org/anthology/2020.alw-1.19 -WS-17,WS-17.39,Investigating Sampling Bias in Abusive Language Detection,Dante Razo|Sandra Kübler,,38939527,https://www.aclweb.org/anthology/2020.alw-1.9 +WS-17,WS-17.37,Detecting East Asian Prejudice on Social Media,Bertie Vidgen|Scott Hale|Ella Guest|Helen Margetts|David Broniatowski|Zeerak Waseem|Austin Botelho|Matthew Hall|Rebekah Tromble,"During COVID-19 concerns have heightened about the spread of aggressive and hateful language online, especially hostility directed against East Asia and East Asian people. We report on a new dataset and the creation of a machine learning classifier that categorizes social media posts from Twitter into four classes: Hostility against East Asia, Criticism of East Asia, Meta-discussions of East Asian prejudice, and a neutral class. The classifier achieves a macro-F1 score of 0.83. We then conduct an in-depth ground-up error analysis and show that the model struggles with edge cases and ambiguous content. We provide the 20,000 tweet training dataset (annotated by experienced analysts), which also contains several secondary categories and additional flags. We also provide the 40,000 original annotations (before adjudication), the full codebook, annotations for COVID-19 relevance and East Asian relevance and stance for 1,000 hashtags, and the final model.",38939526,https://www.aclweb.org/anthology/2020.alw-1.19 +WS-17,WS-17.39,Investigating Sampling Bias in Abusive Language Detection,Dante Razo|Sandra Kübler,"Abusive language detection is becoming increasingly important, but we still understand little about the biases in our datasets for abusive language detection, and how these biases affect the quality of abusive language detection. In the work reported here, we reproduce the investigation of Wiegand et al. (2019) to determine differences between different sampling strategies. They compared boosted random sampling, where abusive posts are upsampled, and biased topic sampling, which focuses on topics that are known to cause abusive language. Instead of comparing individual datasets created using these sampling strategies, we use the sampling strategies on a single, large dataset, thus eliminating the textual source of the dataset as a potential confounding factor. We show that differences in the textual source can have more effect than the chosen sampling strategy.",38939527,https://www.aclweb.org/anthology/2020.alw-1.9 WS-17,WS-17.42,Is your toxicity my toxicity? Understanding the influence of rater identity on perceptions of toxicity,Ian Kivlichan|Olivia Redfield|Rachel Rosen|Raquel Saxe|Nitesh Goyal|Lucy Vasserman,,38939528, WS-17,WS-17.43,Free Expression by Design: Improving in-platform mechanisms and third-party tools to tackle online abuse,Viktorya Vilk|Elodie Vialle|Matt Bailey,,38939529, -WS-17,WS-17.44,HurtBERT: Incorporating Lexical Features with BERT for the Detection of Abusive Language,Anna Koufakou|Endang Wahyu Pamungkas|Valerio Basile|Viviana Patti,,38939530,https://www.aclweb.org/anthology/2020.alw-1.5 -WS-17,WS-17.47,Developing a New Classifier for Automated Identification of Incivility in Social Media,Sam Davidson|Qiusi Sun|Magdalena Wojcieszak,,38939531,https://www.aclweb.org/anthology/2020.alw-1.12 -WS-17,WS-17.48,Abusive Language Detection using Syntactic Dependency Graphs,Kanika Narang|Chris Brew,,38939532,https://www.aclweb.org/anthology/2020.alw-1.6 -WS-17,WS-17.49,Towards a Comprehensive Taxonomy and Large-Scale Annotated Corpus for Online Slur Usage,Jana Kurrek|Haji Mohammad Saleem|Derek Ruths,,38939533,https://www.aclweb.org/anthology/2020.alw-1.17 -WS-17,WS-17.50,Attending the Emotions to Detect Online Abusive Language,Niloofar Safi Samghabadi|Afsheen Hatami|Mahsa Shafaei|Sudipta Kar|Thamar Solorio,,38939534,https://www.aclweb.org/anthology/2020.alw-1.10 -WS-17,WS-17.51,Enhancing the Identification of Cyberbullying through Participant Roles,Gathika Rathnayake|Thushari Atapattu|Mahen Herath|Georgia Zhang|Katrina Falkner,,38939535,https://www.aclweb.org/anthology/2020.alw-1.11 -WS-17,WS-17.52,Using Transfer-based Language Models to Detect Hateful and Offensive Language Online,Vebjørn Isaksen|Björn Gambäck,,38939536,https://www.aclweb.org/anthology/2020.alw-1.3 -WS-17,WS-17.56,On Cross-Dataset Generalization in Automatic Detection of Online Abuse,Isar Nejadgholi|Svetlana Kiritchenko,,38939537,https://www.aclweb.org/anthology/2020.alw-1.20 -WS-17,WS-17.57,Identifying and Measuring Annotator Bias Based on Annotators' Demographic Characteristics,Hala Al Kuwatly|Maximilian Wich|Georg Groh,,38939538,https://www.aclweb.org/anthology/2020.alw-1.21 -WS-17,WS-17.58,Investigating Annotator Bias with a Graph-Based Approach,Maximilian Wich|Hala Al Kuwatly|Georg Groh,,38939539,https://www.aclweb.org/anthology/2020.alw-1.22 -WS-17,WS-17.818,Findings: Hybrid Emoji-Based Masked Language Models for Zero-Shot Abusive Language Detection,Michele Corazza|Stefano Menini|Elena Cabrio|Sara Tonelli|Serena Villata,,38941226,https://www.aclweb.org/anthology/2020.findings-emnlp.84 -WS-18,WS-18.13,Measuring Linguistic Diversity During COVID-19,Jonathan Dunn|Tom Coupe|Benjamin Adams,,38940618,https://www.aclweb.org/anthology/2020.nlpcss-1.1 +WS-17,WS-17.44,HurtBERT: Incorporating Lexical Features with BERT for the Detection of Abusive Language,Anna Koufakou|Endang Wahyu Pamungkas|Valerio Basile|Viviana Patti,"The detection of abusive or offensive remarks in social texts has received significant attention in research. In several related shared tasks, BERT has been shown to be the state-of-the-art. In this paper, we propose to utilize lexical features derived from a hate lexicon towards improving the performance of BERT in such tasks. We explore different ways to utilize the lexical features in the form of lexicon-based encodings at the sentence level or embeddings at the word level. We provide an extensive dataset evaluation that addresses in-domain as well as cross-domain detection of abusive content to render a complete picture. Our results indicate that our proposed models combining BERT with lexical features help improve over a baseline BERT model in many of our in-domain and cross-domain experiments.",38939530,https://www.aclweb.org/anthology/2020.alw-1.5 +WS-17,WS-17.47,Developing a New Classifier for Automated Identification of Incivility in Social Media,Sam Davidson|Qiusi Sun|Magdalena Wojcieszak,"Incivility is not only prevalent on online social media platforms, but also has concrete effects on individual users, online groups, and the platforms themselves. Given the prevalence and effects of online incivility, and the challenges involved in human-based incivility detection, it is urgent to develop validated and versatile automatic approaches to identifying uncivil posts and comments. This project advances both a neural, BERT-based classifier as well as a logistic regression classifier to identify uncivil comments. The classifier is trained on a dataset of Reddit posts, which are annotated for incivility, and further expanded using a combination of labeled data from Reddit and Twitter. Our best performing model achieves an F1 of 0.802 on our Reddit test set. The final model is not only applicable across social media platforms and their distinct data structures, but also computationally versatile, and - as such - ready to be used on vast volumes of online data. All trained models and annotated data are made available to the research community.",38939531,https://www.aclweb.org/anthology/2020.alw-1.12 +WS-17,WS-17.48,Abusive Language Detection using Syntactic Dependency Graphs,Kanika Narang|Chris Brew,"Automated detection of abusive language online has become imperative. Current sequential models (LSTM) do not work well for long and complex sentences while bi-transformer models (BERT) are not computationally efficient for the task. We show that classifiers based on syntactic structure of the text, dependency graphical convolutional networks (DepGCNs) can achieve state-of-the-art performance on abusive language datasets. The overall performance is at par with of strong baselines such as fine-tuned BERT. Further, our GCN-based approach is much more efficient than BERT at inference time making it suitable for real-time detection.",38939532,https://www.aclweb.org/anthology/2020.alw-1.6 +WS-17,WS-17.49,Towards a Comprehensive Taxonomy and Large-Scale Annotated Corpus for Online Slur Usage,Jana Kurrek|Haji Mohammad Saleem|Derek Ruths,"Abusive language classifiers have been shown to exhibit bias against women and racial minorities. Since these models are trained on data that is collected using keywords, they tend to exhibit a high sensitivity towards pejoratives. As a result, comments written by victims of abuse are frequently labelled as hateful, even if they discuss or reclaim slurs. Any attempt to address bias in keyword-based corpora requires a better understanding of pejorative language, as well as an equitable representation of targeted users in data collection. We make two main contributions to this end. First, we provide an annotation guide that outlines 4 main categories of online slur usage, which we further divide into a total of 12 sub-categories. Second, we present a publicly available corpus based on our taxonomy, with 39.8k human annotated comments extracted from Reddit. This corpus was annotated by a diverse cohort of coders, with Shannon equitability indices of 0.90, 0.92, and 0.87 across sexuality, ethnicity, and gender. Taken together, our taxonomy and corpus allow researchers to evaluate classifiers on a wider range of speech containing slurs.",38939533,https://www.aclweb.org/anthology/2020.alw-1.17 +WS-17,WS-17.50,Attending the Emotions to Detect Online Abusive Language,Niloofar Safi Samghabadi|Afsheen Hatami|Mahsa Shafaei|Sudipta Kar|Thamar Solorio,"In recent years, abusive behavior has become a serious issue in online social networks. In this paper, we present a new corpus for the task of abusive language detection that is collected from a semi-anonymous online platform, and unlike the majority of other available resources, is not created based on a specific list of bad words. We also develop computational models to incorporate emotions into textual cues to improve aggression identification. We evaluate our proposed methods on a set of corpora related to the task and show promising results with respect to abusive language detection.",38939534,https://www.aclweb.org/anthology/2020.alw-1.10 +WS-17,WS-17.51,Enhancing the Identification of Cyberbullying through Participant Roles,Gathika Rathnayake|Thushari Atapattu|Mahen Herath|Georgia Zhang|Katrina Falkner,"Cyberbullying is a prevalent social problem that inflicts detrimental consequences to the health and safety of victims such as psychological distress, anti-social behaviour, and suicide. The automation of cyberbullying detection is a recent but widely researched problem, with current research having a strong focus on a binary classification of bullying versus non-bullying. This paper proposes a novel approach to enhancing cyberbullying detection through role modeling. We utilise a dataset from ASKfm to perform multi-class classification to detect participant roles (e.g. victim, harasser). Our preliminary results demonstrate promising performance including 0.83 and 0.76 of F1-score for cyberbullying and role classification respectively, outperforming baselines.",38939535,https://www.aclweb.org/anthology/2020.alw-1.11 +WS-17,WS-17.52,Using Transfer-based Language Models to Detect Hateful and Offensive Language Online,Vebjørn Isaksen|Björn Gambäck,"Distinguishing hate speech from non-hate offensive language is challenging, as hate speech not always includes offensive slurs and offensive language not always express hate. Here, four deep learners based on the Bidirectional Encoder Representations from Transformers (BERT), with either general or domain-specific language models, were tested against two datasets containing tweets labelled as either ‘Hateful’, ‘Normal’ or ‘Offensive’. The results indicate that the attention-based models profoundly confuse hate speech with offensive and normal language. However, the pre-trained models outperform state-of-the-art results in terms of accurately predicting the hateful instances.",38939536,https://www.aclweb.org/anthology/2020.alw-1.3 +WS-17,WS-17.56,On Cross-Dataset Generalization in Automatic Detection of Online Abuse,Isar Nejadgholi|Svetlana Kiritchenko,"NLP research has attained high performances in abusive language detection as a supervised classification task. While in research settings, training and test datasets are usually obtained from similar data samples, in practice systems are often applied on data that are different from the training set in topic and class distributions. Also, the ambiguity in class definitions inherited in this task aggravates the discrepancies between source and target datasets. We explore the topic bias and the task formulation bias in cross-dataset generalization. We show that the benign examples in the Wikipedia Detox dataset are biased towards platform-specific topics. We identify these examples using unsupervised topic modeling and manual inspection of topics’ keywords. Removing these topics increases cross-dataset generalization, without reducing in-domain classification performance. For a robust dataset design, we suggest applying inexpensive unsupervised methods to inspect the collected data and downsize the non-generalizable content before manually annotating for class labels.",38939537,https://www.aclweb.org/anthology/2020.alw-1.20 +WS-17,WS-17.57,Identifying and Measuring Annotator Bias Based on Annotators' Demographic Characteristics,Hala Al Kuwatly|Maximilian Wich|Georg Groh,"Machine learning is recently used to detect hate speech and other forms of abusive language in online platforms. However, a notable weakness of machine learning models is their vulnerability to bias, which can impair their performance and fairness. One type is annotator bias caused by the subjective perception of the annotators. In this work, we investigate annotator bias using classification models trained on data from demographically distinct annotator groups. To do so, we sample balanced subsets of data that are labeled by demographically distinct annotators. We then train classifiers on these subsets, analyze their performances on similarly grouped test sets, and compare them statistically. Our findings show that the proposed approach successfully identifies bias and that demographic features, such as first language, age, and education, correlate with significant performance differences.",38939538,https://www.aclweb.org/anthology/2020.alw-1.21 +WS-17,WS-17.58,Investigating Annotator Bias with a Graph-Based Approach,Maximilian Wich|Hala Al Kuwatly|Georg Groh,"A challenge that many online platforms face is hate speech or any other form of online abuse. To cope with this, hate speech detection systems are developed based on machine learning to reduce manual work for monitoring these platforms. Unfortunately, machine learning is vulnerable to unintended bias in training data, which could have severe consequences, such as a decrease in classification performance or unfair behavior (e.g., discriminating minorities). In the scope of this study, we want to investigate annotator bias — a form of bias that annotators cause due to different knowledge in regards to the task and their subjective perception. Our goal is to identify annotation bias based on similarities in the annotation behavior from annotators. To do so, we build a graph based on the annotations from the different annotators, apply a community detection algorithm to group the annotators, and train for each group classifiers whose performances we compare. By doing so, we are able to identify annotator bias within a data set. The proposed method and collected insights can contribute to developing fairer and more reliable hate speech classification models.",38939539,https://www.aclweb.org/anthology/2020.alw-1.22 +WS-17,WS-17.818,Findings: Hybrid Emoji-Based Masked Language Models for Zero-Shot Abusive Language Detection,Michele Corazza|Stefano Menini|Elena Cabrio|Sara Tonelli|Serena Villata,"Recent studies have demonstrated the effectiveness of cross-lingual language model pre-training on different NLP tasks, such as natural language inference and machine translation. In our work, we test this approach on social media data, which are particularly challenging to process within this framework, since the limited length of the textual messages and the irregularity of the language make it harder to learn meaningful encodings. More specifically, we propose a hybrid emoji-based Masked Language Model (MLM) to leverage the common information conveyed by emojis across different languages and improve the learned cross-lingual representation of short text messages, with the goal to perform zero- shot abusive language detection. We compare the results obtained with the original MLM to the ones obtained by our method, showing improved performance on German, Italian and Spanish.",38941226,https://www.aclweb.org/anthology/2020.findings-emnlp.84 +WS-18,WS-18.13,Measuring Linguistic Diversity During COVID-19,Jonathan Dunn|Tom Coupe|Benjamin Adams,"Computational measures of linguistic diversity help us understand the linguistic landscape using digital language data. The contribution of this paper is to calibrate measures of linguistic diversity using restrictions on international travel resulting from the COVID-19 pandemic. Previous work has mapped the distribution of languages using geo-referenced social media and web data. The goal, however, has been to describe these corpora themselves rather than to make inferences about underlying populations. This paper shows that a difference-in-differences method based on the Herfindahl-Hirschman Index can identify the bias in digital corpora that is introduced by non-local populations. These methods tell us where significant changes have taken place and whether this leads to increased or decreased diversity. This is an important step in aligning digital corpora like social media with the real-world populations that have produced them.",38940618,https://www.aclweb.org/anthology/2020.nlpcss-1.1 WS-18,WS-18.15,How Language Influences Attitudes Toward Brands,David DeFranza|Arul Mishra|Himanshu Mishra,,38940628, -WS-18,WS-18.17,Using BERT for Qualitative Content Analysis in Psychosocial Online Counseling,Philipp Grandeit|Carolyn Haberkern|Maximiliane Lang|Jens Albrecht|Robert Lehmann,,38940609,https://www.aclweb.org/anthology/2020.nlpcss-1.2 -WS-18,WS-18.19,Swimming with the Tide? Positional Claim Detection across Political Text Types,Nico Blokker|Erenay Dayanik|Gabriella Lapesa|Sebastian Padó,,38940616,https://www.aclweb.org/anthology/2020.nlpcss-1.3 -WS-18,WS-18.20,Does Social Support (Expressed in Post Titles) Elicit Comments in Online Substance Use Recovery Forums?,Anietie|Y|Sharath Chandra Guntuku,,38940623,https://www.aclweb.org/anthology/2020.nlpcss-1.4 -WS-18,WS-18.21,I miss you babe: Analyzing Emotion Dynamics During COVID-19 Pandemic,Hui Xian Lynnette Ng|Roy Ka-Wei Lee|Md Rabiul Awal,,38940603,https://www.aclweb.org/anthology/2020.nlpcss-1.5 -WS-18,WS-18.22,"Assessing population-level symptoms of anxiety, depression, and suicide risk in real time using NLP applied to social media data",Alex Fine|Patrick Crutchley|Jenny Blase|Joshua Carroll|Glen Coppersmith,,38940624,https://www.aclweb.org/anthology/2020.nlpcss-1.6 +WS-18,WS-18.17,Using BERT for Qualitative Content Analysis in Psychosocial Online Counseling,Philipp Grandeit|Carolyn Haberkern|Maximiliane Lang|Jens Albrecht|Robert Lehmann,"Qualitative content analysis is a systematic method commonly used in the social sciences to analyze textual data from interviews or online discussions. However, this method usually requires high expertise and manual effort because human coders need to read, interpret, and manually annotate text passages. This is especially true if the system of categories used for annotation is complex and semantically rich. Therefore, qualitative content analysis could benefit greatly from automated coding. In this work, we investigate the usage of machine learning-based text classification models for automatic coding in the area of psycho-social online counseling. We developed a system of over 50 categories to analyze counseling conversations, labeled over 10.000 text passages manually, and evaluated the performance of different machine learning-based classifiers against human coders.",38940609,https://www.aclweb.org/anthology/2020.nlpcss-1.2 +WS-18,WS-18.19,Swimming with the Tide? Positional Claim Detection across Political Text Types,Nico Blokker|Erenay Dayanik|Gabriella Lapesa|Sebastian Padó,"Manifestos are official documents of political parties, providing a comprehensive topical overview of the electoral programs. Voters, however, seldom read them and often prefer other channels, such as newspaper articles, to understand the party positions on various policy issues. The natural question to ask is how compatible these two formats (manifesto and newspaper reports) are in their representation of party positioning. We address this question with an approach that combines political science (manual annotation and analysis) and natural language processing (supervised claim identification) in a cross-text type setting: we train a classifier on annotated newspaper data and test its performance on manifestos. Our findings show a) strong performance for supervised classification even across text types and b) a substantive overlap between the two formats in terms of party positioning, with differences regarding the salience of specific issues.",38940616,https://www.aclweb.org/anthology/2020.nlpcss-1.3 +WS-18,WS-18.20,Does Social Support (Expressed in Post Titles) Elicit Comments in Online Substance Use Recovery Forums?,Anietie|Y|Sharath Chandra Guntuku,"Individuals recovering from substance use often seek social support (emotional and informational) on online recovery forums, where they can both write and comment on posts, expressing their struggles and successes. A common challenge in these forums is that certain posts (some of which may be support seeking) receive no comments. In this work, we use data from two Reddit substance recovery forums: /r/Leaves and /r/OpiatesRecovery, to determine the relationship between the social supports expressed in the titles of posts and the number of comments they receive. We show that the types of social support expressed in post titles that elicit comments vary from one substance use recovery forum to the other.",38940623,https://www.aclweb.org/anthology/2020.nlpcss-1.4 +WS-18,WS-18.21,I miss you babe: Analyzing Emotion Dynamics During COVID-19 Pandemic,Hui Xian Lynnette Ng|Roy Ka-Wei Lee|Md Rabiul Awal,"With the world on a lockdown due to the COVID-19 pandemic, this paper studies emotions expressed on Twitter. Using a combined strategy of time series analysis of emotions augmented by tweet topics, this study provides an insight into emotion transitions during the pandemic. After tweets are annotated with dominant emotions and topics, a time-series emotion analysis is used to identify disgust and anger as the most commonly identified emotions. Through longitudinal analysis of each user, we construct emotion transition graphs, observing key transitions between disgust and anger, and self-transitions within anger and disgust emotional states. Observing user patterns through clustering of user longitudinal analyses reveals emotional transitions fall into four main clusters: (1) erratic motion over short period of time, (2) disgust -> anger, (3) optimism -> joy. (4) erratic motion over a prolonged period. Finally, we propose a method for predicting users subsequent topic, and by consequence their emotions, through constructing an Emotion Topic Hidden Markov Model, augmenting emotion transition states with topic information. Results suggests that the predictions fare better than baselines, spurring directions of predicting emotional states based on Twitter posts.",38940603,https://www.aclweb.org/anthology/2020.nlpcss-1.5 +WS-18,WS-18.22,"Assessing population-level symptoms of anxiety, depression, and suicide risk in real time using NLP applied to social media data",Alex Fine|Patrick Crutchley|Jenny Blase|Joshua Carroll|Glen Coppersmith,"Prevailing methods for assessing population-level mental health require costly collection of large samples of data through instruments such as surveys, and are thus slow to reflect current, rapidly changing social conditions. This constrains how easily population-level mental health data can be integrated into health and policy decision-making. Here, we demonstrate that natural language processing applied to publicly-available social media data can provide real-time estimates of psychological distress in the population (specifically, English-speaking Twitter users in the US). We examine population-level changes in linguistic correlates of mental health symptoms in response to the COVID-19 pandemic and to the killing of George Floyd. As a case study, we focus on social media data from healthcare providers, compared to a control sample. Our results provide a concrete demonstration of how the tools of computational social science can be applied to provide real-time or near-real-time insight into the impact of public events on mental health.",38940624,https://www.aclweb.org/anthology/2020.nlpcss-1.6 WS-18,WS-18.23,Topic preference detection: A novel approach to understand perspective taking in conversation,Michael Yeomans|Alison Wood Brooks,,38940626, -WS-18,WS-18.26,Viable Threat on News Reading: Generating Biased News Using Natural Language Models,Saurabh Gupta|Hong Huy Nguyen|Junichi Yamagishi|Isao Echizen,,38940610,https://www.aclweb.org/anthology/2020.nlpcss-1.7 +WS-18,WS-18.26,Viable Threat on News Reading: Generating Biased News Using Natural Language Models,Saurabh Gupta|Hong Huy Nguyen|Junichi Yamagishi|Isao Echizen,"Recent advancements in natural language generation has raised serious concerns. High-performance language models are widely used for language generation tasks because they are able to produce fluent and meaningful sentences. These models are already being used to create fake news. They can also be exploited to generate biased news, which can then be used to attack news aggregators to change their reader’s behavior and influence their bias. In this paper, we use a threat model to demonstrate that the publicly available language models can reliably generate biased news content based on an input original news. We also show that a large number of high-quality biased news articles can be generated using controllable text generation. A subjective evaluation with 80 participants demonstrated that the generated biased news is generally fluent, and a bias evaluation with 24 participants demonstrated that the bias (left or right) is usually evident in the generated articles and can be easily identified.",38940610,https://www.aclweb.org/anthology/2020.nlpcss-1.7 WS-18,WS-18.28,A Lexical Semantic Leadership Network of Nineteenth CenturyAbolitionist Newspapers,Sandeep Soni|Lauren Klein|Jacob Eisenstein,,38940625, -WS-18,WS-18.29,Unsupervised Anomaly Detection in Parole Hearings using Language Models,Authors: Graham Todd|Catalin Voss|Jenny Hong,,38940611,https://www.aclweb.org/anthology/2020.nlpcss-1.8 -WS-18,WS-18.32,Identifying Worry in Twitter: Beyond Emotion Analysis,Reyha Verma|Christian Von Der Weth|Jithin Vachery|Mohan Kankanhalli,,38940602,https://www.aclweb.org/anthology/2020.nlpcss-1.9 -WS-18,WS-18.33,"Text Zoning and Classification for Job Advertisements in German, French and English",Ann-Sophie Gnehm|Simon Clematide,,38940604,https://www.aclweb.org/anthology/2020.nlpcss-1.10 -WS-18,WS-18.34,Is Wikipedia succeeding in reducing gender bias? Assessing changes in gender bias in Wikipedia using word embeddings,Katja Geertruida Schmahl|Tom Julian Viering|Stavros Makrodimitris|Arman Naseri Jahfari|David Tax|Marco Loog,,38940605,https://www.aclweb.org/anthology/2020.nlpcss-1.11 -WS-18,WS-18.36,Effects of Anonymity on Comment Persuasiveness in Wikipedia Articles for Deletion Discussions,Yimin Xiao|Lu Xiao,,38940619,https://www.aclweb.org/anthology/2020.nlpcss-1.12 -WS-18,WS-18.37,"Uncertainty over Uncertainty: Investigating the Assumptions, Annotations, and Text Measurements of Economic Policy Uncertainty",Katherine Keith|Christoph Teichmann|Brendan O'Connor|Edgar Meij,,38940620,https://www.aclweb.org/anthology/2020.nlpcss-1.13 -WS-18,WS-18.38,Recalibrating classifiers for interpretable abusive content detection,Bertie Vidgen|Scott Hale|Sam Staton|Tom Melham|Helen Margetts|Ohad Kammar|Marcin Szymczak,,38940621,https://www.aclweb.org/anthology/2020.nlpcss-1.14 -WS-18,WS-18.43,Predicting independent living outcomes from written reports of social workers,Angelika Maier|Philipp Cimiano,,38940617,https://www.aclweb.org/anthology/2020.nlpcss-1.15 -WS-18,WS-18.44,Analyzing Political Bias and Unfairness in News Articles at Different Levels of Granularity,Wei-Fan Chen|Khalid Al Khatib|Henning Wachsmuth|Benno Stein,,38940612,https://www.aclweb.org/anthology/2020.nlpcss-1.16 -WS-18,WS-18.45,Mapping Local News Coverage: Precise location extraction in textual news content using fine-tuned BERT based language model,Sarang Gupta|Kumari Nishu,,38940613,https://www.aclweb.org/anthology/2020.nlpcss-1.17 -WS-18,WS-18.47,Foreigner-directed speech is simpler than native-directed: Evidence from social media,Aleksandrs Berdicevskis,,38940614,https://www.aclweb.org/anthology/2020.nlpcss-1.18 -WS-18,WS-18.49,Diachronic Embeddings for People in the News,Felix Hennig|Steven Wilson,,38940606,https://www.aclweb.org/anthology/2020.nlpcss-1.19 -WS-18,WS-18.50,Social media data as a lens onto care-seeking behavior among women veterans of the US armed forces,Kacie Kelly|Alex Fine|Glen Coppersmith,,38940615,https://www.aclweb.org/anthology/2020.nlpcss-1.20 -WS-18,WS-18.51,Understanding Weekly COVID-19 Concerns through Dynamic Content-Specific LDA Topic Modeling,Mohammadzaman Zamani|H.|Rew Schwartz|Johannes Eichstaedt|Sharath Chandra Guntuku|Adithya Virinchipuram Ganesan|Sean Clouston|Salvatore Giorgi,,38940607,https://www.aclweb.org/anthology/2020.nlpcss-1.21 -WS-18,WS-18.52,Emoji and Self-Identity in Twitter Bios,Jinhang Li|Giorgos Longinos|Steven Wilson|Walid Magdy,,38940622,https://www.aclweb.org/anthology/2020.nlpcss-1.22 -WS-18,WS-18.53,Analyzing Gender Bias within Narrative Tropes,Dhruvil Gala|Mohammad Omar Khursheed|Hannah Lerner|Brendan O'Connor|Mohit Iyyer,,38940608,https://www.aclweb.org/anthology/2020.nlpcss-1.23 +WS-18,WS-18.29,Unsupervised Anomaly Detection in Parole Hearings using Language Models,Authors: Graham Todd|Catalin Voss|Jenny Hong,"Each year, thousands of roughly 150-page parole hearing transcripts in California go unread because legal experts lack the time to review them. Yet, reviewing transcripts is the only means of public oversight in the parole process. To assist reviewers, we present a simple unsupervised technique for using language models (LMs) to identify procedural anomalies in long-form legal text. Our technique highlights unusual passages that suggest further review could be necessary. We utilize a contrastive perplexity score to identify passages, defined as the scaled difference between its perplexities from two LMs, one fine-tuned on the target (parole) domain, and another pre-trained on out-of-domain text to normalize for grammatical or syntactic anomalies. We present quantitative analysis of the results and note that our method has identified some important cases for review. We are also excited about potential applications in unsupervised anomaly detection, and present a brief analysis of results for detecting fake TripAdvisor reviews.",38940611,https://www.aclweb.org/anthology/2020.nlpcss-1.8 +WS-18,WS-18.32,Identifying Worry in Twitter: Beyond Emotion Analysis,Reyha Verma|Christian Von Der Weth|Jithin Vachery|Mohan Kankanhalli,"Identifying the worries of individuals and societies plays a crucial role in providing social support and enhancing policy decision-making. Due to the popularity of social media platforms such as Twitter, users share worries about personal issues (e.g., health, finances, relationships) and broader issues (e.g., changes in society, environmental concerns, terrorism) freely. In this paper, we explore and evaluate a wide range of machine learning models to predict worry on Twitter. While this task has been closely associated with emotion prediction, we argue and show that identifying worry needs to be addressed as a separate task given the unique challenges associated with it. We conduct a user study to provide evidence that social media posts express two basic kinds of worry – normative and pathological – as stated in psychology literature. In addition, we show that existing emotion detection techniques underperform, especially while capturing normative worry. Finally, we discuss the current limitations of our approach and propose future applications of the worry identification system.",38940602,https://www.aclweb.org/anthology/2020.nlpcss-1.9 +WS-18,WS-18.33,"Text Zoning and Classification for Job Advertisements in German, French and English",Ann-Sophie Gnehm|Simon Clematide,"We present experiments to structure job ads into text zones and classify them into pro- fessions, industries and management functions, thereby facilitating social science analyses on labor marked demand. Our main contribution are empirical findings on the benefits of contextualized embeddings and the potential of multi-task models for this purpose. With contextualized in-domain embeddings in BiLSTM-CRF models, we reach an accuracy of 91% for token-level text zoning and outperform previous approaches. A multi-tasking BERT model performs well for our classification tasks. We further compare transfer approaches for our multilingual data.",38940604,https://www.aclweb.org/anthology/2020.nlpcss-1.10 +WS-18,WS-18.34,Is Wikipedia succeeding in reducing gender bias? Assessing changes in gender bias in Wikipedia using word embeddings,Katja Geertruida Schmahl|Tom Julian Viering|Stavros Makrodimitris|Arman Naseri Jahfari|David Tax|Marco Loog,"Large text corpora used for creating word embeddings (vectors which represent word meanings) often contain stereotypical gender biases. As a result, such unwanted biases will typically also be present in word embeddings derived from such corpora and downstream applications in the field of natural language processing (NLP). To minimize the effect of gender bias in these settings, more insight is needed when it comes to where and how biases manifest themselves in the text corpora employed. This paper contributes by showing how gender bias in word embeddings from Wikipedia has developed over time. Quantifying the gender bias over time shows that art related words have become more female biased. Family and science words have stereotypical biases towards respectively female and male words. These biases seem to have decreased since 2006, but these changes are not more extreme than those seen in random sets of words. Career related words are more strongly associated with male than with female, this difference has only become smaller in recently written articles. These developments provide additional understanding of what can be done to make Wikipedia more gender neutral and how important time of writing can be when considering biases in word embeddings trained from Wikipedia or from other text corpora.",38940605,https://www.aclweb.org/anthology/2020.nlpcss-1.11 +WS-18,WS-18.36,Effects of Anonymity on Comment Persuasiveness in Wikipedia Articles for Deletion Discussions,Yimin Xiao|Lu Xiao,"It has been shown that anonymity affects various aspects of online communications such as message credibility, the trust among communicators, and the participants’ accountability and reputation. Anonymity influences social interactions in online communities in these many ways, which can lead to influences on opinion change and the persuasiveness of a message. Prior studies also suggest that the effect of anonymity can vary in different online communication contexts and online communities. In this study, we focus on Wikipedia Articles for Deletion (AfD) discussions as an example of online collaborative communities to study the relationship between anonymity and persuasiveness in this context. We find that in Wikipedia AfD discussions, more identifiable users tend to be more persuasive. The higher persuasiveness can be related to multiple aspects, including linguistic features of the comments, the user’s motivation to participate, persuasive skills the user learns over time, and the user’s identity and credibility established in the community through participation.",38940619,https://www.aclweb.org/anthology/2020.nlpcss-1.12 +WS-18,WS-18.37,"Uncertainty over Uncertainty: Investigating the Assumptions, Annotations, and Text Measurements of Economic Policy Uncertainty",Katherine Keith|Christoph Teichmann|Brendan O'Connor|Edgar Meij,"Methods and applications are inextricably linked in science, and in particular in the domain of text-as-data. In this paper, we examine one such text-as-data application, an established economic index that measures economic policy uncertainty from keyword occurrences in news. This index, which is shown to correlate with firm investment, employment, and excess market returns, has had substantive impact in both the private sector and academia. Yet, as we revisit and extend the original authors’ annotations and text measurements we find interesting text-as-data methodological research questions: (1) Are annotator disagreements a reflection of ambiguity in language? (2) Do alternative text measurements correlate with one another and with measures of external predictive validity? We find for this application (1) some annotator disagreements of economic policy uncertainty can be attributed to ambiguity in language, and (2) switching measurements from keyword-matching to supervised machine learning classifiers results in low correlation, a concerning implication for the validity of the index.",38940620,https://www.aclweb.org/anthology/2020.nlpcss-1.13 +WS-18,WS-18.38,Recalibrating classifiers for interpretable abusive content detection,Bertie Vidgen|Scott Hale|Sam Staton|Tom Melham|Helen Margetts|Ohad Kammar|Marcin Szymczak,"We investigate the use of machine learning classifiers for detecting online abuse in empirical research. We show that uncalibrated classifiers (i.e. where the ‘raw’ scores are used) align poorly with human evaluations. This limits their use for understanding the dynamics, patterns and prevalence of online abuse. We examine two widely used classifiers (created by Perspective and Davidson et al.) on a dataset of tweets directed against candidates in the UK’s 2017 general election. A Bayesian approach is presented to recalibrate the raw scores from the classifiers, using probabilistic programming and newly annotated data. We argue that interpretability evaluation and recalibration is integral to the application of abusive content classifiers.",38940621,https://www.aclweb.org/anthology/2020.nlpcss-1.14 +WS-18,WS-18.43,Predicting independent living outcomes from written reports of social workers,Angelika Maier|Philipp Cimiano,"In social care environments, the main goal of social workers is to foster independent living by their clients. An important task is thus to monitor progress towards reaching independence in different areas of their patients’ life. To support this task, we present an approach that extracts indications of independence on different life aspects from the day-to-day documentation that social workers create. We describe the process of collecting and annotating a corresponding corpus created from data records of two social work institutions with a focus on disability care. We show that the agreement on the task of annotating the observations of social workers with respect to discrete independent levels yields a high agreement of .74 as measured by Fleiss’ Kappa. We present a classification approach towards automatically classifying an observation into the discrete independence levels and present results for different types of classifiers. Against our original expectation, we show that we reach F-Measures (macro) of 95% averaged across topics, showing that this task can be automatically solved.",38940617,https://www.aclweb.org/anthology/2020.nlpcss-1.15 +WS-18,WS-18.44,Analyzing Political Bias and Unfairness in News Articles at Different Levels of Granularity,Wei-Fan Chen|Khalid Al Khatib|Henning Wachsmuth|Benno Stein,"Media is an indispensable source of information and opinion, shaping the beliefs and attitudes of our society. Obviously, media portals can also provide overly biased content, e.g., by reporting on political events in a selective or incomplete manner. A relevant question hence is whether and how such a form of unfair news coverage can be exposed. This paper addresses the automatic detection of bias, but it goes one step further in that it explores how political bias and unfairness are manifested linguistically. We utilize a new corpus of 6964 news articles with labels derived from adfontesmedia.com to develop a neural model for bias assessment. Analyzing the model on article excerpts, we find insightful bias patterns at different levels of text granularity, from single words to the whole article discourse.",38940612,https://www.aclweb.org/anthology/2020.nlpcss-1.16 +WS-18,WS-18.45,Mapping Local News Coverage: Precise location extraction in textual news content using fine-tuned BERT based language model,Sarang Gupta|Kumari Nishu,"Mapping local news coverage from textual content is a challenging problem that requires extracting precise location mentions from news articles. While traditional named entity taggers are able to extract geo-political entities and certain non geo-political entities, they cannot recognize precise location mentions such as addresses, streets and intersections that are required to accurately map the news article. We fine-tune a BERT-based language model for achieving high level of granularity in location extraction. We incorporate the model into an end-to-end tool that further geocodes the extracted locations for the broader objective of mapping news coverage.",38940613,https://www.aclweb.org/anthology/2020.nlpcss-1.17 +WS-18,WS-18.47,Foreigner-directed speech is simpler than native-directed: Evidence from social media,Aleksandrs Berdicevskis,"I test two hypotheses that play an important role in modern sociolinguistics and language evolution studies: first, that non-native production is simpler than native; second, that production addressed to non-native speakers is simpler than that addressed to natives. The second hypothesis is particularly important for theories about contact-induced simplification, since the accommodation to non-natives may explain how the simplification can spread from adult learners to the whole community. To test the hypotheses, I create a very large corpus of native and non-native written speech in four languages (English, French, Italian, Spanish), extracting data from an internet forum where native languages of the participants are known and the structure of the interactions can be inferred. The corpus data yield inconsistent evidence with respect to the first hypothesis, but largely support the second one, suggesting that foreigner-directed speech is indeed simpler than native-directed. Importantly, when testing the first hypothesis, I contrast production of different speakers, which can introduce confounds and is a likely reason for the inconsistencies. When testing the second hypothesis, the comparison is always within the production of the same speaker (but with different addressees), which makes it more reliable.",38940614,https://www.aclweb.org/anthology/2020.nlpcss-1.18 +WS-18,WS-18.49,Diachronic Embeddings for People in the News,Felix Hennig|Steven Wilson,"Previous English-language diachronic change models based on word embeddings have typically used single tokens to represent entities, including names of people. This leads to issues with both ambiguity (resulting in one embedding representing several distinct and unrelated people) and unlinked references (leading to several distinct embeddings which represent the same person). In this paper, we show that using named entity recognition and heuristic name linking steps before training a diachronic embedding model leads to more accurate representations of references to people, as compared to the token-only baseline. In large news corpus of articles from The Guardian, we provide examples of several types of analysis that can be performed using these new embeddings. Further, we show that real world events and context changes can be detected using our proposed model.",38940606,https://www.aclweb.org/anthology/2020.nlpcss-1.19 +WS-18,WS-18.50,Social media data as a lens onto care-seeking behavior among women veterans of the US armed forces,Kacie Kelly|Alex Fine|Glen Coppersmith,"In this article, we examine social media data as a lens onto support-seeking among women veterans of the US armed forces. Social media data hold a great deal of promise as a source of information on needs and support-seeking among individuals who are excluded from or systematically prevented from accessing clinical or other institutions ostensibly designed to support them. We apply natural language processing (NLP) techniques to more than 3 million Tweets collected from 20,000 Twitter users. We find evidence that women veterans are more likely to use social media to seek social and community engagement and to discuss mental health and veterans’ issues significantly more frequently than their male counterparts. By contrast, male veterans tend to use social media to amplify political ideologies or to engage in partisan debate. Our results have implications for how organizations can provide outreach and services to this uniquely vulnerable population, and illustrate the utility of non-traditional observational data sources such as social media to understand the needs of marginalized groups.",38940615,https://www.aclweb.org/anthology/2020.nlpcss-1.20 +WS-18,WS-18.51,Understanding Weekly COVID-19 Concerns through Dynamic Content-Specific LDA Topic Modeling,Mohammadzaman Zamani|H.|Rew Schwartz|Johannes Eichstaedt|Sharath Chandra Guntuku|Adithya Virinchipuram Ganesan|Sean Clouston|Salvatore Giorgi,"The novelty and global scale of the COVID-19 pandemic has lead to rapid societal changes in a short span of time. As government policy and health measures shift, public perceptions and concerns also change, an evolution documented within discourse on social media.We propose a dynamic content-specific LDA topic modeling technique that can help to identify different domains of COVID-specific discourse that can be used to track societal shifts in concerns or views. Our experiments show that these model-derived topics are more coherent than standard LDA topics, and also provide new features that are more helpful in prediction of COVID-19 related outcomes including social mobility and unemployment rate.",38940607,https://www.aclweb.org/anthology/2020.nlpcss-1.21 +WS-18,WS-18.52,Emoji and Self-Identity in Twitter Bios,Jinhang Li|Giorgos Longinos|Steven Wilson|Walid Magdy,"Emoji are widely used to express emotions and concepts on social media, and prior work has shown that users’ choice of emoji reflects the way that they wish to present themselves to the world. Emoji usage is typically studied in the context of posts made by users, and this view has provided important insights into phenomena such as emotional expression and self-representation. In addition to making posts, however, social media platforms like Twitter allow for users to provide a short bio, which is an opportunity to briefly describe their account as a whole. In this work, we focus on the use of emoji in these bio statements. We explore the ways in which users include emoji in these self-descriptions, finding different patterns than those observed around emoji usage in tweets. We examine the relationships between emoji used in bios and the content of users’ tweets, showing that the topics and even the average sentiment of tweets varies for users with different emoji in their bios. Lastly, we confirm that homophily effects exist with respect to the types of emoji that are included in bios of users and their followers.",38940622,https://www.aclweb.org/anthology/2020.nlpcss-1.22 +WS-18,WS-18.53,Analyzing Gender Bias within Narrative Tropes,Dhruvil Gala|Mohammad Omar Khursheed|Hannah Lerner|Brendan O'Connor|Mohit Iyyer,"Popular media reflects and reinforces societal biases through the use of tropes, which are narrative elements, such as archetypal characters and plot arcs, that occur frequently across media. In this paper, we specifically investigate gender bias within a large collection of tropes. To enable our study, we crawl tvtropes.org, an online user-created repository that contains 30K tropes associated with 1.9M examples of their occurrences across film, television, and literature. We automatically score the “genderedness” of each trope in our TVTROPES dataset, which enables an analysis of (1) highly-gendered topics within tropes, (2) the relationship between gender bias and popular reception, and (3) how the gender of a work’s creator correlates with the types of tropes that they use.",38940608,https://www.aclweb.org/anthology/2020.nlpcss-1.23 WS-18,WS-18.57,An Unfair Affinity Toward Fairness: Characterizing 70 Years of Social Biases in B^Hollywood,Kunal Khadilkar|Ashiqur KhudaBukhsh,,38940627, -WS-19,WS-19.12,Evaluation of Machine Translation Methods applied to Medical Terminologies,Konstantinos Skianis|Yann Briand|Florent Desgrippes,,38940042,https://www.aclweb.org/anthology/2020.louhi-1.7 -WS-19,WS-19.13,Not a cute stroke: Analysis of Rule- and Neural Network-based Information Extraction Systems for Brain Radiology Reports,Andreas Grivas|Beatrice Alex|Claire Grover|Richard Tobin|William Whiteley,,38940043,https://www.aclweb.org/anthology/2020.louhi-1.4 -WS-19,WS-19.14,Biomedical Event Extraction as Multi-turn Question Answering,Xing David Wang|Leon Weber|Ulf Leser,,38940044,https://www.aclweb.org/anthology/2020.louhi-1.10 -WS-19,WS-19.15,GGPONC: A Corpus of German Medical Text with Rich Metadata Based on Clinical Practice Guidelines,Florian Borchert|Christina Lohr|Luise Modersohn|Thomas Langer|Markus Follmann|Jan Philipp Sachs|Udo Hahn|Matthieu-P. Schapranow,,38940045,https://www.aclweb.org/anthology/2020.louhi-1.5 -WS-19,WS-19.17,Medical Concept Normalization in User-Generated Texts by Learning Target Concept Embeddings,Katikapalli Subramanyam Kalyan|Sivanesan Sangeetha,,38940046,https://www.aclweb.org/anthology/2020.louhi-1.3 -WS-19,WS-19.2,The Impact of De-identification on Downstream Named Entity Recognition in Clinical Text,Hanna Berg|Aron Henriksson|Hercules Dalianis,,38940038,https://www.aclweb.org/anthology/2020.louhi-1.1 -WS-19,WS-19.24,Defining and Learning Refined Temporal Relations in the Clinical Narrative,Chen Lin|Kristin Wright-Bettner|Timothy Miller|Steven Bethard|Dmitriy Dligach|Martha Palmer|James H. Martin|Guergana Savova,,38940047,https://www.aclweb.org/anthology/2020.louhi-1.12 -WS-19,WS-19.26,Simple Hierarchical Multi-Task Neural End-To-End Entity Linking for Biomedical Text,Maciej Wiatrak|Juha Iso-Sipila,,38940048,https://www.aclweb.org/anthology/2020.louhi-1.2 -WS-19,WS-19.27,Detection of Mental Health from Reddit via Deep Contextualized Representations,Zhengping Jiang|Sarah Ita Levitan|Jonathan Zomick|Julia Hirschberg,,38940049,https://www.aclweb.org/anthology/2020.louhi-1.16 +WS-19,WS-19.12,Evaluation of Machine Translation Methods applied to Medical Terminologies,Konstantinos Skianis|Yann Briand|Florent Desgrippes,"Medical terminologies resources and standards play vital roles in clinical data exchanges, enabling significantly the services’ interoperability within healthcare national information networks. Health and medical science are constantly evolving causing requirements to advance the terminologies editions. In this paper, we present our evaluation work of the latest machine translation techniques addressing medical terminologies. Experiments have been conducted leveraging selected statistical and neural machine translation methods. The devised procedure is tested on a validated sample of ICD-11 and ICF terminologies from English to French with promising results.",38940042,https://www.aclweb.org/anthology/2020.louhi-1.7 +WS-19,WS-19.13,Not a cute stroke: Analysis of Rule- and Neural Network-based Information Extraction Systems for Brain Radiology Reports,Andreas Grivas|Beatrice Alex|Claire Grover|Richard Tobin|William Whiteley,"We present an in-depth comparison of three clinical information extraction (IE) systems designed to perform entity recognition and negation detection on brain imaging reports: EdIE-R, a bespoke rule-based system, and two neural network models, EdIE-BiLSTM and EdIE-BERT, both multi-task learning models with a BiLSTM and BERT encoder respectively. We compare our models both on an in-sample and an out-of-sample dataset containing mentions of stroke findings and draw on our error analysis to suggest improvements for effective annotation when building clinical NLP models for a new domain. Our analysis finds that our rule-based system outperforms the neural models on both datasets and seems to generalise to the out-of-sample dataset. On the other hand, the neural models do not generalise negation to the out-of-sample dataset, despite metrics on the in-sample dataset suggesting otherwise.",38940043,https://www.aclweb.org/anthology/2020.louhi-1.4 +WS-19,WS-19.14,Biomedical Event Extraction as Multi-turn Question Answering,Xing David Wang|Leon Weber|Ulf Leser,"Biomedical event extraction from natural text is a challenging task as it searches for complex and often nested structures describing specific relationships between multiple molecular entities, such as genes, proteins, or cellular components. It usually is implemented by a complex pipeline of individual tools to solve the different relation extraction subtasks. We present an alternative approach where the detection of relationships between entities is described uniformly as questions, which are iteratively answered by a question answering (QA) system based on the domain-specific language model SciBERT. This model outperforms two strong baselines in two biomedical event extraction corpora in a Knowledge Base Population setting, and also achieves competitive performance in BioNLP challenge evaluation settings.",38940044,https://www.aclweb.org/anthology/2020.louhi-1.10 +WS-19,WS-19.15,GGPONC: A Corpus of German Medical Text with Rich Metadata Based on Clinical Practice Guidelines,Florian Borchert|Christina Lohr|Luise Modersohn|Thomas Langer|Markus Follmann|Jan Philipp Sachs|Udo Hahn|Matthieu-P. Schapranow,"The lack of publicly accessible text corpora is a major obstacle for progress in natural language processing. For medical applications, unfortunately, all language communities other than English are low-resourced. In this work, we present GGPONC (German Guideline Program in Oncology NLP Corpus), a freely dis tributable German language corpus based on clinical practice guidelines for oncology. This corpus is one of the largest ever built from German medical documents. Unlike clinical documents, clinical guidelines do not contain any patient-related information and can therefore be used without data protection restrictions. Moreover, GGPONC is the first corpus for the German language covering diverse conditions in a large medical subfield and provides a variety of metadata, such as literature references and evidence levels. By applying and evaluating existing medical information extraction pipelines for German text, we are able to draw comparisons for the use of medical language to other corpora, medical and non-medical ones.",38940045,https://www.aclweb.org/anthology/2020.louhi-1.5 +WS-19,WS-19.17,Medical Concept Normalization in User-Generated Texts by Learning Target Concept Embeddings,Katikapalli Subramanyam Kalyan|Sivanesan Sangeetha,"Medical concept normalization helps in discovering standard concepts in free-form text i.e., maps health-related mentions to standard concepts in a clinical knowledge base. It is much beyond simple string matching and requires a deep semantic understanding of concept mentions. Recent research approach concept normalization as either text classification or text similarity. The main drawback in existing a) text classification approach is ignoring valuable target concepts information in learning input concept mention representation b) text similarity approach is the need to separately generate target concept embeddings which is time and resource consuming. Our proposed model overcomes these drawbacks by jointly learning the representations of input concept mention and target concepts. First, we learn input concept mention representation using RoBERTa. Second, we find cosine similarity between embeddings of input concept mention and all the target concepts. Here, embeddings of target concepts are randomly initialized and then updated during training. Finally, the target concept with maximum cosine similarity is assigned to the input concept mention. Our model surpasses all the existing methods across three standard datasets by improving accuracy up to 2.31%.",38940046,https://www.aclweb.org/anthology/2020.louhi-1.3 +WS-19,WS-19.2,The Impact of De-identification on Downstream Named Entity Recognition in Clinical Text,Hanna Berg|Aron Henriksson|Hercules Dalianis,"The impact of de-identification on data quality and, in particular, utility for developing models for downstream tasks has been more thoroughly studied for structured data than for unstructured text. While previous studies indicate that text de-identification has a limited impact on models for downstream tasks, it remains unclear what the impact is with various levels and forms of de-identification, in particular concerning the trade-off between precision and recall. In this paper, the impact of de-identification is studied on downstream named entity recognition in Swedish clinical text. The results indicate that de-identification models with moderate to high precision lead to similar downstream performance, while low precision has a substantial negative impact. Furthermore, different strategies for concealing sensitive information affect performance to different degrees, ranging from pseudonymisation having a low impact to the removal of entire sentences with sensitive information having a high impact. This study indicates that it is possible to increase the recall of models for identifying sensitive information without negatively affecting the use of de-identified text data for training models for clinical named entity recognition; however, there is ultimately a trade-off between the level of de-identification and the subsequent utility of the data.",38940038,https://www.aclweb.org/anthology/2020.louhi-1.1 +WS-19,WS-19.24,Defining and Learning Refined Temporal Relations in the Clinical Narrative,Chen Lin|Kristin Wright-Bettner|Timothy Miller|Steven Bethard|Dmitriy Dligach|Martha Palmer|James H. Martin|Guergana Savova,"We present refinements over existing temporal relation annotations in the Electronic Medical Record clinical narrative. We refined the THYME corpus annotations to more faithfully represent nuanced temporality and nuanced temporal-coreferential relations. The main contributions are in re-defining CONTAINS and OVERLAP relations into CONTAINS, CONTAINS-SUBEVENT, OVERLAP and NOTED-ON. We demonstrate that these refinements lead to substantial gains in learnability for state-of-the-art transformer models as compared to previously reported results on the original THYME corpus. We thus establish a baseline for the automatic extraction of these refined temporal relations. Although our study is done on clinical narrative, we believe it addresses far-reaching challenges that are corpus- and domain- agnostic.",38940047,https://www.aclweb.org/anthology/2020.louhi-1.12 +WS-19,WS-19.26,Simple Hierarchical Multi-Task Neural End-To-End Entity Linking for Biomedical Text,Maciej Wiatrak|Juha Iso-Sipila,"Recognising and linking entities is a crucial first step to many tasks in biomedical text analysis, such as relation extraction and target identification. Traditionally, biomedical entity linking methods rely heavily on heuristic rules and predefined, often domain-specific features. The features try to capture the properties of entities and complex multi-step architectures to detect, and subsequently link entity mentions. We propose a significant simplification to the biomedical entity linking setup that does not rely on any heuristic methods. The system performs all the steps of the entity linking task jointly in either single or two stages. We explore the use of hierarchical multi-task learning, using mention recognition and entity typing tasks as auxiliary tasks. We show that hierarchical multi-task models consistently outperform single-task models when trained tasks are homogeneous. We evaluate the performance of our models on the biomedical entity linking benchmarks using MedMentions and BC5CDR datasets. We achieve state-of-theart results on the challenging MedMentions dataset, and comparable results on BC5CDR.",38940048,https://www.aclweb.org/anthology/2020.louhi-1.2 +WS-19,WS-19.27,Detection of Mental Health from Reddit via Deep Contextualized Representations,Zhengping Jiang|Sarah Ita Levitan|Jonathan Zomick|Julia Hirschberg,"We address the problem of automatic detection of psychiatric disorders from the linguistic content of social media posts. We build a large scale dataset of Reddit posts from users with eight disorders and a control user group. We extract and analyze linguistic characteristics of posts and identify differences between diagnostic groups. We build strong classification models based on deep contextualized word representations and show that they outperform previously applied statistical models with simple linguistic features by large margins. We compare user-level and post-level classification performance, as well as an ensembled multiclass model.",38940049,https://www.aclweb.org/anthology/2020.louhi-1.16 WS-19,WS-19.28,Information retrieval for animal disease surveillance: a pattern-based approach,Sarah Valentin|Renaud Lancelot|Mathieu Roche,,38940050, -WS-19,WS-19.32,Identifying Personal Experience Tweets of Medication Effects Using Pre-trained RoBERTa Language Model and Its Updating,Minghao Zhu|Youzhe Song|Ge Jin|Keyuan Jiang,,38940051,https://www.aclweb.org/anthology/2020.louhi-1.14 -WS-19,WS-19.35,Detecting Foodborne Illness Complaints in Multiple Languages Using English Annotations Only,Ziyi Liu|Giannis Karamanolakis|Daniel Hsu|Luis Gravano,,38940052,https://www.aclweb.org/anthology/2020.louhi-1.15 -WS-19,WS-19.4,Normalization of Long-tail Adverse Drug Reactions in Social Media,Emmanouil Manousogiannis|Sepideh Mesbah|Alessandro Bozzon|Robert-Jan Sips|Zoltan Szlanik|Selene Baez,,38940039,https://www.aclweb.org/anthology/2020.louhi-1.6 -WS-19,WS-19.44,Context-Aware Automatic Text Simplification of Health Materials in Low-Resource Domains,Tarek Sakakini|Jong Yoon Lee|Aditya Duri|Renato F.L. Azevedo|Victor Sadauskas|Kuangxiao Gu|Suma Bhat|Dan Morrow|James Graumlich|Saqib Walayat|Mark Hasegawa-Johnson|Thomas Huang|Ann Willemsen-Dunlap|Donald Halpin,,38940053,https://www.aclweb.org/anthology/2020.louhi-1.13 -WS-19,WS-19.5,Multitask Learning of Negation and Speculation using Transformers,Aditya Khandelwal|Benita Kathleen Britto,,38940040,https://www.aclweb.org/anthology/2020.louhi-1.9 -WS-19,WS-19.8,An efficient representation of chronological events in medical texts,Andrey Kormilitzin|Nemanja Vaci|Qiang Liu|Hao Ni|Goran Nenadic|Alejo Nevado-Holgado,,38940041,https://www.aclweb.org/anthology/2020.louhi-1.11 +WS-19,WS-19.32,Identifying Personal Experience Tweets of Medication Effects Using Pre-trained RoBERTa Language Model and Its Updating,Minghao Zhu|Youzhe Song|Ge Jin|Keyuan Jiang,"Post-market surveillance, the practice of monitoring the safe use of pharmaceutical drugs is an important part of pharmacovigilance. Being able to collect personal experience related to pharmaceutical product use could help us gain insight into how the human body reacts to different medications. Twitter, a popular social media service, is being considered as an important alternative data source for collecting personal experience information with medications. Identifying personal experience tweets is a challenging classification task in natural language processing. In this study, we utilized three methods based on Facebook’s Robustly Optimized BERT Pretraining Approach (RoBERTa) to predict personal experience tweets related to medication use: the first one combines the pre-trained RoBERTa model with a classifier, the second combines the updated pre-trained RoBERTa model using a corpus of unlabeled tweets with a classifier, and the third combines the RoBERTa model that was trained with our unlabeled tweets from scratch with the classifier too. Our results show that all of these approaches outperform the published methods (Word Embedding + LSTM) in classification performance (p < 0.05), and updating the pre-trained language model with tweets related to medications could even improve the performance further.",38940051,https://www.aclweb.org/anthology/2020.louhi-1.14 +WS-19,WS-19.35,Detecting Foodborne Illness Complaints in Multiple Languages Using English Annotations Only,Ziyi Liu|Giannis Karamanolakis|Daniel Hsu|Luis Gravano,"Health departments have been deploying text classification systems for the early detection of foodborne illness complaints in social media documents such as Yelp restaurant reviews. Current systems have been successfully applied for documents in English and, as a result, a promising direction is to increase coverage and recall by considering documents in additional languages, such as Spanish or Chinese. Training previous systems for more languages, however, would be expensive, as it would require the manual annotation of many documents for each new target language. To address this challenge, we consider cross-lingual learning and train multilingual classifiers using only the annotations for English-language reviews. Recent zero-shot approaches based on pre-trained multi-lingual BERT (mBERT) have been shown to effectively align languages for aspects such as sentiment. Interestingly, we show that those approaches are less effective for capturing the nuances of foodborne illness, our public health application of interest. To improve performance without extra annotations, we create artificial training documents in the target language through machine translation and train mBERT jointly for the source (English) and target language. Furthermore, we show that translating labeled documents to multiple languages leads to additional performance improvements for some target languages. We demonstrate the benefits of our approach through extensive experiments with Yelp restaurant reviews in seven languages. Our classifiers identify foodborne illness complaints in multilingual reviews from the Yelp Challenge dataset, which highlights the potential of our general approach for deployment in health departments.",38940052,https://www.aclweb.org/anthology/2020.louhi-1.15 +WS-19,WS-19.4,Normalization of Long-tail Adverse Drug Reactions in Social Media,Emmanouil Manousogiannis|Sepideh Mesbah|Alessandro Bozzon|Robert-Jan Sips|Zoltan Szlanik|Selene Baez,"The automatic mapping of Adverse Drug Reaction (ADR) reports from user-generated content to concepts in a controlled medical vocabulary provides valuable insights for monitoring public health. While state-of-the-art deep learning-based sequence classification techniques achieve impressive performance for medical concepts with large amounts of training data, they show their limit with long-tail concepts that have a low number of training samples. The above hinders their adaptability to the changes of layman’s terminology and the constant emergence of new informal medical terms. Our objective in this paper is to tackle the problem of normalizing long-tail ADR mentions in user-generated content. In this paper, we exploit the implicit semantics of rare ADRs for which we have few training samples, in order to detect the most similar class for the given ADR. The evaluation results demonstrate that our proposed approach addresses the limitations of the existing techniques when the amount of training data is limited.",38940039,https://www.aclweb.org/anthology/2020.louhi-1.6 +WS-19,WS-19.44,Context-Aware Automatic Text Simplification of Health Materials in Low-Resource Domains,Tarek Sakakini|Jong Yoon Lee|Aditya Duri|Renato F.L. Azevedo|Victor Sadauskas|Kuangxiao Gu|Suma Bhat|Dan Morrow|James Graumlich|Saqib Walayat|Mark Hasegawa-Johnson|Thomas Huang|Ann Willemsen-Dunlap|Donald Halpin,"Healthcare systems have increased patients’ exposure to their own health materials to enhance patients’ health levels, but this has been impeded by patients’ lack of understanding of their health material. We address potential barriers to their comprehension by developing a context-aware text simplification system for health material. Given the scarcity of annotated parallel corpora in healthcare domains, we design our system to be independent of a parallel corpus, complementing the availability of data-driven neural methods when such corpora are available. Our system compensates for the lack of direct supervision using a biomedical lexical database: Unified Medical Language System (UMLS). Compared to a competitive prior approach that uses a tool for identifying biomedical concepts and a consumer-directed vocabulary list, we empirically show the enhanced accuracy of our system due to improved handling of ambiguous terms. We also show the enhanced accuracy of our system over directly-supervised neural methods in this low-resource setting. Finally, we show the direct impact of our system on laypeople’s comprehension of health material via a human subjects’ study (n=160).",38940053,https://www.aclweb.org/anthology/2020.louhi-1.13 +WS-19,WS-19.5,Multitask Learning of Negation and Speculation using Transformers,Aditya Khandelwal|Benita Kathleen Britto,"Detecting negation and speculation in language has been a task of considerable interest to the biomedical community, as it is a key component of Information Extraction systems from Biomedical documents. Prior work has individually addressed Negation Detection and Speculation Detection, and both have been addressed in the same way, using 2 stage pipelined approach: Cue Detection followed by Scope Resolution. In this paper, we propose Multitask learning approaches over 2 sets of tasks: Negation Cue Detection & Speculation Cue Detection, and Negation Scope Resolution & Speculation Scope Resolution. We utilise transformer-based architectures like BERT, XLNet and RoBERTa as our core model architecture, and finetune these using the Multitask learning approaches. We show that this Multitask Learning approach outperforms the single task learning approach, and report new state-of-the-art results on Negation and Speculation Scope Resolution on the BioScope Corpus and the SFU Review Corpus.",38940040,https://www.aclweb.org/anthology/2020.louhi-1.9 +WS-19,WS-19.8,An efficient representation of chronological events in medical texts,Andrey Kormilitzin|Nemanja Vaci|Qiang Liu|Hao Ni|Goran Nenadic|Alejo Nevado-Holgado,"In this work we addressed the problem of capturing sequential information contained in longitudinal electronic health records (EHRs). Clinical notes, which is a particular type of EHR data, are a rich source of information and practitioners often develop clever solutions how to maximise the sequential information contained in free-texts. We proposed a systematic methodology for learning from chronological events available in clinical notes. The proposed methodological path signature framework creates a non-parametric hierarchical representation of sequential events of any type and can be used as features for downstream statistical learning tasks. The methodology was developed and externally validated using the largest in the UK secondary care mental health EHR data on a specific task of predicting survival risk of patients diagnosed with Alzheimer’s disease. The signature-based model was compared to a common survival random forest model. Our results showed a 15.4% increase of risk prediction AUC at the time point of 20 months after the first admission to a specialist memory clinic and the signature method outperformed the baseline mixed-effects model by 13.2 %.",38940041,https://www.aclweb.org/anthology/2020.louhi-1.11 WS-2,WS-2.1,The DiDi Machine Translation System for WMT20,Tanfang Chen|Weiwei Wang|Wenyang Wei|Xing Shi|Xiangang Li|Jieping Ye|Kevin Knight,,38939543, WS-2,WS-2.100,Score Combination for Improved Parallel Corpus Filtering for Low Resource Conditions,Muhammad ElNokrashy|Amr Hendy|Mohamed Abdelghaffar|Mohamed Afify|Ahmed Tawfik|Hany Hassan Awadalla,,38939612, WS-2,WS-2.101,Data Selection for Unsupervised Translation of German–Upper Sorbian,Lukas Edman|Antonio Toral|Gertjan Van Noord,,38939613, @@ -552,108 +552,108 @@ WS-26,WS-26.63,Improved Topic Representations of Medical Documents to Assist COV WS-26,WS-26.64,Automatic Evaluation vs. User Preference in Neural Textual QuestionAnswering over COVID-19 Scientific Literature,Arantxa Otegi|Jon Ander Campos|Gorka Azkune|Aitor Soroa|Eneko Agirre,,38939858, WS-26,WS-26.65,Twitter Data Augmentation for Monitoring Public Opinion on COVID-19 Intervention Measures,Lin Miao|Mark Last|Marina Litvak,,38939859, WS-26,WS-26.9,CAiRE-COVID: A Question Answering and Query-focused Multi-Document Summarization System for COVID-19 Scholarly Information Management,Dan Su|Yan Xu|Tiezheng Yu|Farhad Bin Siddique|Elham Barezi|Pascale Fung,,38939844, -WS-3,WS-3.1,Domain adaptation challenges of BERT in tokenization and sub-word representations of Out-of-Vocabulary words,Anmol Nayak|Hariprasad Timmapathini|Karthikeyan Ponnalagu|Vijendran Gopalan Venkoparao,,38940788,https://www.aclweb.org/anthology/2020.insights-1.1 -WS-3,WS-3.12,On Task-Level Dialogue Composition of Generative Transformer Model,Prasanna Parthasarathi|Sharan Narang|Arvind Neelakantan,,38940793,https://www.aclweb.org/anthology/2020.insights-1.6 -WS-3,WS-3.19,How Effectively Can Machines Defend Against Machine-Generated Fake News? An Empirical Study,Meghana Moorthy Bhat|Srinivasan Parthasarathy,,38940794,https://www.aclweb.org/anthology/2020.insights-1.7 -WS-3,WS-3.20,Label Propagation-Based Semi-Supervised Learning for Hate Speech Classification,Ashwin Geet D'Sa|Irina Illina|Dominique Fohr|Dietrich Klakow,,38940795,https://www.aclweb.org/anthology/2020.insights-1.8 -WS-3,WS-3.22,Layout-Aware Text Representations Harm Clustering Documents by Type,Catherine Finegan-Dollak|Ashish Verma,,38940796,https://www.aclweb.org/anthology/2020.insights-1.9 -WS-3,WS-3.23,An Analysis of Capsule Networks for Part of Speech Tagging in High- and Low-resource Scenarios,Andrew Zupon|Faiz Rafique|Mihai Surdeanu,,38940797,https://www.aclweb.org/anthology/2020.insights-1.10 -WS-3,WS-3.24,Can Knowledge Graph Embeddings Tell Us What Fact-checked Claims Are About?,Valentina Beretta|Sébastien Harispe|Katarina Boland|Luke Lo Seen|Konstantin Todorov|On Tchechmedjiev,,38940798,https://www.aclweb.org/anthology/2020.insights-1.11 -WS-3,WS-3.2436,WER we are and WER we think we are,Piotr Szymański|Piotr Żelasko|Mikolaj Morzy|Adrian Szymczak|Marzena Żyła-Hoppe|Joanna Banaszczak|Lukasz Augustyniak|Jan Mizgajski|Yishay Carmiel,,38940634,https://www.aclweb.org/anthology/2020.findings-emnlp.295 -WS-3,WS-3.2575-ws3,What do we expect from Multiple-choice QA Systems?,Krunal Shah|Nitish Gupta|Dan Roth,,38940809,https://www.aclweb.org/anthology/2020.findings-emnlp.317 -WS-3,WS-3.26,"Do Transformers Dream of Inference, or Can Pretrained Generative Models Learn Implicit Inferential Rules?",Zhengzhong Liang|Mihai Surdeanu,,38940799,https://www.aclweb.org/anthology/2020.insights-1.12 -WS-3,WS-3.27,Counterfactually-Augmented SNLI Training Data Does Not Yield Better Generalization Than Unaugmented Data,William Huang|Haokun Liu|Samuel R. Bowman,,38940800,https://www.aclweb.org/anthology/2020.insights-1.13 -WS-3,WS-3.29,NMF Ensembles? Not for Text Summarization!,Alka Khurana|Vasudha Bhatnagar,,38940801,https://www.aclweb.org/anthology/2020.insights-1.14 -WS-3,WS-3.30,"If You Build Your Own NER Scorer, Non-replicable Results Will Come",Constantine Lignos|Marjan Kamyab,,38940802,https://www.aclweb.org/anthology/2020.insights-1.15 -WS-3,WS-3.3004,Effects of Naturalistic Variation in Goal-Oriented Dialog,Jatin Ganhotra|Robert Moore|Sachindra Joshi|Kahini Wadhawan,,38940807,https://www.aclweb.org/anthology/2020.findings-emnlp.358 -WS-3,WS-3.31,HINT3: Raising the bar for Intent Detection in the Wild,Gaurav Arora|Chirag Jain|Manas Chaturvedi|Krupal Modi,,38940803,https://www.aclweb.org/anthology/2020.insights-1.16 +WS-3,WS-3.1,Domain adaptation challenges of BERT in tokenization and sub-word representations of Out-of-Vocabulary words,Anmol Nayak|Hariprasad Timmapathini|Karthikeyan Ponnalagu|Vijendran Gopalan Venkoparao,"BERT model (Devlin et al., 2019) has achieved significant progress in several Natural Language Processing (NLP) tasks by leveraging the multi-head self-attention mechanism (Vaswani et al., 2017) in its architecture. However, it still has several research challenges which are not tackled well for domain specific corpus found in industries. In this paper, we have highlighted these problems through detailed experiments involving analysis of the attention scores and dynamic word embeddings with the BERT-Base-Uncased model. Our experiments have lead to interesting findings that showed: 1) Largest substring from the left that is found in the vocabulary (in-vocab) is always chosen at every sub-word unit that can lead to suboptimal tokenization choices, 2) Semantic meaning of a vocabulary word deteriorates when found as a substring in an Out-Of-Vocabulary (OOV) word, and 3) Minor misspellings in words are inadequately handled. We believe that if these challenges are tackled, it will significantly help the domain adaptation aspect of BERT.",38940788,https://www.aclweb.org/anthology/2020.insights-1.1 +WS-3,WS-3.12,On Task-Level Dialogue Composition of Generative Transformer Model,Prasanna Parthasarathi|Sharan Narang|Arvind Neelakantan,"Task-oriented dialogue systems help users accomplish tasks such as booking a movie ticket and ordering food via conversation. Generative models parameterized by a deep neural network are widely used for next turn response generation in such systems. It is natural for users of the system to want to accomplish multiple tasks within the same conversation, but the ability of generative models to compose multiple tasks is not well studied. In this work, we begin by studying the effect of training human-human task-oriented dialogues towards improving the ability to compose multiple tasks on Transformer generative models. To that end, we propose and explore two solutions: (1) creating synthetic multiple task dialogue data for training from human-human single task dialogue and (2) forcing the encoder representation to be invariant to single and multiple task dialogues using an auxiliary loss. The results from our experiments highlight the difficulty of even the sophisticated variant of transformer model in learning to compose multiple tasks from single task dialogues.",38940793,https://www.aclweb.org/anthology/2020.insights-1.6 +WS-3,WS-3.19,How Effectively Can Machines Defend Against Machine-Generated Fake News? An Empirical Study,Meghana Moorthy Bhat|Srinivasan Parthasarathy,"We empirically study the effectiveness of machine-generated fake news detectors by understanding the model’s sensitivity to different synthetic perturbations during test time. The current machine-generated fake news detectors rely on provenance to determine the veracity of news. Our experiments find that the success of these detectors can be limited since they are rarely sensitive to semantic perturbations and are very sensitive to syntactic perturbations. Also, we would like to open-source our code and believe it could be a useful diagnostic tool for evaluating models aimed at fighting machine-generated fake news.",38940794,https://www.aclweb.org/anthology/2020.insights-1.7 +WS-3,WS-3.20,Label Propagation-Based Semi-Supervised Learning for Hate Speech Classification,Ashwin Geet D'Sa|Irina Illina|Dominique Fohr|Dietrich Klakow,"Research on hate speech classification has received increased attention. In real-life scenarios, a small amount of labeled hate speech data is available to train a reliable classifier. Semi-supervised learning takes advantage of a small amount of labeled data and a large amount of unlabeled data. In this paper, label propagation-based semi-supervised learning is explored for the task of hate speech classification. The quality of labeling the unlabeled set depends on the input representations. In this work, we show that pre-trained representations are label agnostic, and when used with label propagation yield poor results. Neural network-based fine-tuning can be adopted to learn task-specific representations using a small amount of labeled data. We show that fully fine-tuned representations may not always be the best representations for the label propagation and intermediate representations may perform better in a semi-supervised setup.",38940795,https://www.aclweb.org/anthology/2020.insights-1.8 +WS-3,WS-3.22,Layout-Aware Text Representations Harm Clustering Documents by Type,Catherine Finegan-Dollak|Ashish Verma,"Clustering documents by type—grouping invoices with invoices and articles with articles—is a desirable first step for organizing large collections of document scans. Humans approaching this task use both the semantics of the text and the document layout to assist in grouping like documents. LayoutLM (Xu et al., 2019), a layout-aware transformer built on top of BERT with state-of-the-art performance on document-type classification, could reasonably be expected to outperform regular BERT (Devlin et al., 2018) for document-type clustering. However, we find experimentally that BERT significantly outperforms LayoutLM on this task (p <0.001). We analyze clusters to show where layout awareness is an asset and where it is a liability.",38940796,https://www.aclweb.org/anthology/2020.insights-1.9 +WS-3,WS-3.23,An Analysis of Capsule Networks for Part of Speech Tagging in High- and Low-resource Scenarios,Andrew Zupon|Faiz Rafique|Mihai Surdeanu,"Neural networks are a common tool in NLP, but it is not always clear which architecture to use for a given task. Different tasks, different languages, and different training conditions can all affect how a neural network will perform. Capsule Networks (CapsNets) are a relatively new architecture in NLP. Due to their novelty, CapsNets are being used more and more in NLP tasks. However, their usefulness is still mostly untested.In this paper, we compare three neural network architectures—LSTM, CNN, and CapsNet—on a part of speech tagging task. We compare these architectures in both high- and low-resource training conditions and find that no architecture consistently performs the best. Our analysis shows that our CapsNet performs nearly as well as a more complex LSTM under certain training conditions, but not others, and that our CapsNet almost always outperforms our CNN. We also find that our CapsNet implementation shows faster prediction times than the LSTM for Scottish Gaelic but not for Spanish, highlighting the effect that the choice of languages can have on the models.",38940797,https://www.aclweb.org/anthology/2020.insights-1.10 +WS-3,WS-3.24,Can Knowledge Graph Embeddings Tell Us What Fact-checked Claims Are About?,Valentina Beretta|Sébastien Harispe|Katarina Boland|Luke Lo Seen|Konstantin Todorov|On Tchechmedjiev,"The web offers a wealth of discourse data that help researchers from various fields analyze debates about current societal issues and gauge the effects on society of important phenomena such as misinformation spread. Such analyses often revolve around claims made by people about a given topic of interest. Fact-checking portals offer partially structured information that can assist such analysis. However, exploiting the network structure of such online discourse data is as of yet under-explored. We study the effectiveness of using neural-graph embedding features for claim topic prediction and their complementarity with text embeddings. We show that graph embeddings are modestly complementary with text embeddings, but the low performance of graph embedding features alone indicate that the model fails to capture topological features pertinent of the topic prediction task.",38940798,https://www.aclweb.org/anthology/2020.insights-1.11 +WS-3,WS-3.2436,WER we are and WER we think we are,Piotr Szymański|Piotr Żelasko|Mikolaj Morzy|Adrian Szymczak|Marzena Żyła-Hoppe|Joanna Banaszczak|Lukasz Augustyniak|Jan Mizgajski|Yishay Carmiel,"Natural language processing of conversational speech requires the availability of high-quality transcripts. In this paper, we express our skepticism towards the recent reports of very low Word Error Rates (WERs) achieved by modern Automatic Speech Recognition (ASR) systems on benchmark datasets. We outline several problems with popular benchmarks and compare three state-of-the-art commercial ASR systems on an internal dataset of real-life spontaneous human conversations and HUB’05 public benchmark. We show that WERs are significantly higher than the best reported results. We formulate a set of guidelines which may aid in the creation of real-life, multi-domain datasets with high quality annotations for training and testing of robust ASR systems.",38940634,https://www.aclweb.org/anthology/2020.findings-emnlp.295 +WS-3,WS-3.2575-ws3,What do we expect from Multiple-choice QA Systems?,Krunal Shah|Nitish Gupta|Dan Roth,"The recent success of machine learning systems on various QA datasets could be interpreted as a significant improvement in models’ language understanding abilities. However, using various perturbations, multiple recent works have shown that good performance on a dataset might not indicate performance that correlates well with human’s expectations from models that “understand” language. In this work we consider a top performing model on several Multiple Choice Question Answering (MCQA) datasets, and evaluate it against a set of expectations one might have from such a model, using a series of zero-information perturbations of the model’s inputs. Our results show that the model clearly falls short of our expectations, and motivates a modified training approach that forces the model to better attend to the inputs. We show that the new training paradigm leads to a model that performs on par with the original model while better satisfying our expectations.",38940809,https://www.aclweb.org/anthology/2020.findings-emnlp.317 +WS-3,WS-3.26,"Do Transformers Dream of Inference, or Can Pretrained Generative Models Learn Implicit Inferential Rules?",Zhengzhong Liang|Mihai Surdeanu,"Large pretrained language models (LM) have been used successfully for multi-hop question answering. However, most of these directions are not interpretable, as they do not make the inference hops necessary to explain a candidate answer explicitly. In this work, we investigate the capability of a state-of-the-art transformer LM to generate explicit inference hops, i.e., to infer a new statement necessary to answer a question given some premise input statements. Our analysis shows that such LMs can generate new statements for some simple inference types, but performance remains poor for complex, real-world inference types such as those that require monotonicity, composition, and commonsense knowledge.",38940799,https://www.aclweb.org/anthology/2020.insights-1.12 +WS-3,WS-3.27,Counterfactually-Augmented SNLI Training Data Does Not Yield Better Generalization Than Unaugmented Data,William Huang|Haokun Liu|Samuel R. Bowman,"A growing body of work shows that models exploit annotation artifacts to achieve state-of-the-art performance on standard crowdsourced benchmarks—datasets collected from crowdworkers to create an evaluation task—while still failing on out-of-domain examples for the same task. Recent work has explored the use of counterfactually-augmented data—data built by minimally editing a set of seed examples to yield counterfactual labels—to augment training data associated with these benchmarks and build more robust classifiers that generalize better. However, Khashabi et al. (2020) find that this type of augmentation yields little benefit on reading comprehension tasks when controlling for dataset size and cost of collection. We build upon this work by using English natural language inference data to test model generalization and robustness and find that models trained on a counterfactually-augmented SNLI dataset do not generalize better than unaugmented datasets of similar size and that counterfactual augmentation can hurt performance, yielding models that are less robust to challenge examples. Counterfactual augmentation of natural language understanding data through standard crowdsourcing techniques does not appear to be an effective way of collecting training data and further innovation is required to make this general line of work viable.",38940800,https://www.aclweb.org/anthology/2020.insights-1.13 +WS-3,WS-3.29,NMF Ensembles? Not for Text Summarization!,Alka Khurana|Vasudha Bhatnagar,"Non-negative Matrix Factorization (NMF) has been used for text analytics with promising results. Instability of results arising due to stochastic variations during initialization makes a case for use of ensemble technology. However, our extensive empirical investigation indicates otherwise. In this paper, we establish that ensemble summary for single document using NMF is no better than the best base model summary.",38940801,https://www.aclweb.org/anthology/2020.insights-1.14 +WS-3,WS-3.30,"If You Build Your Own NER Scorer, Non-replicable Results Will Come",Constantine Lignos|Marjan Kamyab,"We attempt to replicate a named entity recognition (NER) model implemented in a popular toolkit and discover that a critical barrier to doing so is the inconsistent evaluation of improper label sequences. We define these sequences and examine how two scorers differ in their handling of them, finding that one approach produces F1 scores approximately 0.5 points higher on the CoNLL 2003 English development and test sets. We propose best practices to increase the replicability of NER evaluations by increasing transparency regarding the handling of improper label sequences.",38940802,https://www.aclweb.org/anthology/2020.insights-1.15 +WS-3,WS-3.3004,Effects of Naturalistic Variation in Goal-Oriented Dialog,Jatin Ganhotra|Robert Moore|Sachindra Joshi|Kahini Wadhawan,"Existing benchmarks used to evaluate the performance of end-to-end neural dialog systems lack a key component: natural variation present in human conversations. Most datasets are constructed through crowdsourcing, where the crowd workers follow a fixed template of instructions while enacting the role of a user/agent. This results in straight-forward, somewhat routine, and mostly trouble-free conversations, as crowd workers do not think to represent the full range of actions that occur naturally with real users. In this work, we investigate the impact of naturalistic variation on two goal-oriented datasets: bAbI dialog task and Stanford Multi-Domain Dataset (SMD). We also propose new and more effective testbeds for both datasets, by introducing naturalistic variation by the user. We observe that there is a significant drop in performance (more than 60% in Ent. F1 on SMD and 85% in per-dialog accuracy on bAbI task) of recent state-of-the-art end-to-end neural methods such as BossNet and GLMP on both datasets.",38940807,https://www.aclweb.org/anthology/2020.findings-emnlp.358 +WS-3,WS-3.31,HINT3: Raising the bar for Intent Detection in the Wild,Gaurav Arora|Chirag Jain|Manas Chaturvedi|Krupal Modi,"Intent Detection systems in the real world are exposed to complexities of imbalanced datasets containing varying perception of intent, unintended correlations and domain-specific aberrations. To facilitate benchmarking which can reflect near real-world scenarios, we introduce 3 new datasets created from live chatbots in diverse domains. Unlike most existing datasets that are crowdsourced, our datasets contain real user queries received by the chatbots and facilitates penalising unwanted correlations grasped during the training process. We evaluate 4 NLU platforms and a BERT based classifier and find that performance saturates at inadequate levels on test sets because all systems latch on to unintended patterns in training data.",38940803,https://www.aclweb.org/anthology/2020.insights-1.16 WS-3,WS-3.3106,Poison Attacks against Text Datasets with Conditional Adversarially Regularized Autoencoder,Alvin Chan|Yi Tay|Yew-Soon Ong|Aston Zhang,"This paper demonstrates a fatal vulnerability in natural language inference (NLI) and text classification systems. More concretely, we present a ‘backdoor poisoning’ attack on NLP models. Our poisoning attack utilizes conditional adversarially regularized autoencoder (CARA) to generate poisoned training samples by poison injection in latent space. Just by adding 1% poisoned data, our experiments show that a victim BERT finetuned classifier’s predictions can be steered to the poison target class with success rates of >80\% when the input hypothesis is injected with the poison signature, demonstrating that NLI and text classification systems face a huge security risk.",38940808,https://www.aclweb.org/anthology/2020.findings-emnlp.373 -WS-3,WS-3.3296,How Effective is Task-Agnostic Data Augmentation for Pretrained Transformers?,Shayne Longpre|Yu Wang|Christopher DuBois,,38940806,https://www.aclweb.org/anthology/2020.findings-emnlp.394 -WS-3,WS-3.33,The Extraordinary Failure of Complement Coercion Crowdsourcing,Yanai Elazar|Victoria Basmov|Shauli Ravfogel|Yoav Goldberg|Reut Tsarfat,,38940804,https://www.aclweb.org/anthology/2020.insights-1.17 -WS-3,WS-3.34,Embedding Structured Dictionary Entries,Steven Wilson|Walid Magdy|Barbara McGillivray|Gareth Tyson,,38940805,https://www.aclweb.org/anthology/2020.insights-1.18 -WS-3,WS-3.4,Q. Can Knowledge Graphs be used to Answer Boolean Questions? A. It's complicated!,Daria Dzendzik|Carl Vogel|Jennifer Foster,,38940789,https://www.aclweb.org/anthology/2020.insights-1.2 -WS-3,WS-3.6,How Far Can We Go with Data Selection? A Case Study on Semantic Sequence Tagging Tasks,Samuel Louvan|Bernardo Magnini,,38940790,https://www.aclweb.org/anthology/2020.insights-1.3 -WS-3,WS-3.7,Evaluating the Effectiveness of Efficient Neural Architecture Search for Sentence-Pair Tasks,Ansel MacLaughlin|Jwala Dhamala|Anoop Kumar|Sriram Venkatapathy|Ragav Venkatesan|Rahul Gupta,,38940791,https://www.aclweb.org/anthology/2020.insights-1.4 -WS-3,WS-3.8,Which Matters Most? Comparing the Impact of Concept and Document Relationships in Topic Models,Silvia Terragni|Debora Nozza|Elisabetta Fersini|Messina Enza,,38940792,https://www.aclweb.org/anthology/2020.insights-1.5 -WS-4,WS-4.1175,Filtering before Iteratively Referring for Knowledge-Grounded Response Selection in Retrieval-Based Chatbots,Jia-Chen Gu,,38940705,https://www.aclweb.org/anthology/2020.findings-emnlp.127 -WS-4,WS-4.1735,TSDG: Content-aware Neural Response Generation with Two-stage Decoding Process,Junsheng Kong|Zhicheng Zhong|Yi Cai|Xin Wu|Da Ren,,38940706,https://www.aclweb.org/anthology/2020.findings-emnlp.192 -WS-4,WS-4.1761-ws4,ConveRT: Efficient and Accurate Conversational Representations from Transformers,Ivan Vulić,,38940707,https://www.aclweb.org/anthology/2020.findings-emnlp.196 -WS-4,WS-4.1947,Learning Improvised Chatbots from Adversarial Modifications of Natural Language Feedback,Makesh Narsimhan Sreedhar|Kun Ni|Siva Reddy,,38940708,https://www.aclweb.org/anthology/2020.findings-emnlp.221 -WS-4,WS-4.2041,Toward Stance-based Personas for Opinionated Dialogues,Thomas Scialom|Serra Sinem Tekiroglu|Jacopo Staiano|Marco Guerini,,38940704,https://www.aclweb.org/anthology/2020.findings-emnlp.238 -WS-4,WS-4.2889,Joint Turn and Dialogue level User Satisfaction Estimation on Multi-Domain Conversations,Praveen Kumar Bodigutla|Aditya Tiwari|Josep Vallas Vargas|Lazaros Polymenakos|Spyros Matsoukas,,38940709,https://www.aclweb.org/anthology/2020.findings-emnlp.347 -WS-4,WS-4.2957,Making Information Seeking Easier: An Improved Pipeline for Conversational Search,Vaibhav Kumar|Jamie Callan,,38940710,https://www.aclweb.org/anthology/2020.findings-emnlp.354 -WS-4,WS-4.3361,SMRT Chatbots: Improving Non-Task-Oriented Dialog with Simulated Multi-Reference Training,Huda Khayrallah|João Sedoc,,38940711,https://www.aclweb.org/anthology/2020.findings-emnlp.403 +WS-3,WS-3.3296,How Effective is Task-Agnostic Data Augmentation for Pretrained Transformers?,Shayne Longpre|Yu Wang|Christopher DuBois,"Task-agnostic forms of data augmentation have proven widely effective in computer vision, even on pretrained models. In NLP similar results are reported most commonly for low data regimes, non-pretrained models, or situationally for pretrained models. In this paper we ask how effective these techniques really are when applied to pretrained transformers. Using two popular varieties of task-agnostic data augmentation (not tailored to any particular task), Easy Data Augmentation (Wei andZou, 2019) and Back-Translation (Sennrichet al., 2015), we conduct a systematic examination of their effects across 5 classification tasks, 6 datasets, and 3 variants of modern pretrained transformers, including BERT, XLNet, and RoBERTa. We observe a negative result, finding that techniques which previously reported strong improvements for non-pretrained models fail to consistently improve performance for pretrained transformers, even when training data is limited. We hope this empirical analysis helps inform practitioners where data augmentation techniques may confer improvements.",38940806,https://www.aclweb.org/anthology/2020.findings-emnlp.394 +WS-3,WS-3.33,The Extraordinary Failure of Complement Coercion Crowdsourcing,Yanai Elazar|Victoria Basmov|Shauli Ravfogel|Yoav Goldberg|Reut Tsarfat,"Crowdsourcing has eased and scaled up the collection of linguistic annotation in recent years. In this work, we follow known methodologies of collecting labeled data for the complement coercion phenomenon. These are constructions with an implied action — e.g., “I started a new book I bought last week”, where the implied action is reading. We aim to collect annotated data for this phenomenon by reducing it to either of two known tasks: Explicit Completion and Natural Language Inference. However, in both cases, crowdsourcing resulted in low agreement scores, even though we followed the same methodologies as in previous work. Why does the same process fail to yield high agreement scores? We specify our modeling schemes, highlight the differences with previous work and provide some insights about the task and possible explanations for the failure. We conclude that specific phenomena require tailored solutions, not only in specialized algorithms, but also in data collection methods.",38940804,https://www.aclweb.org/anthology/2020.insights-1.17 +WS-3,WS-3.34,Embedding Structured Dictionary Entries,Steven Wilson|Walid Magdy|Barbara McGillivray|Gareth Tyson,"Previous work has shown how to effectively use external resources such as dictionaries to improve English-language word embeddings, either by manipulating the training process or by applying post-hoc adjustments to the embedding space. We experiment with a multi-task learning approach for explicitly incorporating the structured elements of dictionary entries, such as user-assigned tags and usage examples, when learning embeddings for dictionary headwords. Our work generalizes several existing models for learning word embeddings from dictionaries. However, we find that the most effective representations overall are learned by simply training with a skip-gram objective over the concatenated text of all entries in the dictionary, giving no particular focus to the structure of the entries.",38940805,https://www.aclweb.org/anthology/2020.insights-1.18 +WS-3,WS-3.4,Q. Can Knowledge Graphs be used to Answer Boolean Questions? A. It's complicated!,Daria Dzendzik|Carl Vogel|Jennifer Foster,"In this paper we explore the problem of machine reading comprehension, focusing on the BoolQ dataset of Yes/No questions. We carry out an error analysis of a BERT-based machine reading comprehension model on this dataset, revealing issues such as unstable model behaviour and some noise within the dataset itself. We then experiment with two approaches for integrating information from knowledge graphs: (i) concatenating knowledge graph triples to text passages and (ii) encoding knowledge with a Graph Neural Network. Neither of these approaches show a clear improvement and we hypothesize that this may be due to a combination of inaccuracies in the knowledge graph, imprecision in entity linking, and the models’ inability to capture additional information from knowledge graphs.",38940789,https://www.aclweb.org/anthology/2020.insights-1.2 +WS-3,WS-3.6,How Far Can We Go with Data Selection? A Case Study on Semantic Sequence Tagging Tasks,Samuel Louvan|Bernardo Magnini,"Although several works have addressed the role of data selection to improve transfer learning for various NLP tasks, there is no consensus about its real benefits and, more generally, there is a lack of shared practices on how it can be best applied. We propose a systematic approach aimed at evaluating data selection in scenarios of increasing complexity. Specifically, we compare the case in which source and target tasks are the same while source and target domains are different, against the more challenging scenario where both tasks and domains are different. We run a number of experiments on semantic sequence tagging tasks, which are relatively less investigated in data selection, and conclude that data selection has more benefit on the scenario when the tasks are the same, while in case of different (although related) tasks from distant domains, a combination of data selection and multi-task learning is ineffective for most cases.",38940790,https://www.aclweb.org/anthology/2020.insights-1.3 +WS-3,WS-3.7,Evaluating the Effectiveness of Efficient Neural Architecture Search for Sentence-Pair Tasks,Ansel MacLaughlin|Jwala Dhamala|Anoop Kumar|Sriram Venkatapathy|Ragav Venkatesan|Rahul Gupta,"Neural Architecture Search (NAS) methods, which automatically learn entire neural model or individual neural cell architectures, have recently achieved competitive or state-of-the-art (SOTA) performance on variety of natural language processing and computer vision tasks, including language modeling, natural language inference, and image classification. In this work, we explore the applicability of a SOTA NAS algorithm, Efficient Neural Architecture Search (ENAS) (Pham et al., 2018) to two sentence pair tasks, paraphrase detection and semantic textual similarity. We use ENAS to perform a micro-level search and learn a task-optimized RNN cell architecture as a drop-in replacement for an LSTM. We explore the effectiveness of ENAS through experiments on three datasets (MRPC, SICK, STS-B), with two different models (ESIM, BiLSTM-Max), and two sets of embeddings (Glove, BERT). In contrast to prior work applying ENAS to NLP tasks, our results are mixed – we find that ENAS architectures sometimes, but not always, outperform LSTMs and perform similarly to random architecture search.",38940791,https://www.aclweb.org/anthology/2020.insights-1.4 +WS-3,WS-3.8,Which Matters Most? Comparing the Impact of Concept and Document Relationships in Topic Models,Silvia Terragni|Debora Nozza|Elisabetta Fersini|Messina Enza,"Topic models have been widely used to discover hidden topics in a collection of documents. In this paper, we propose to investigate the role of two different types of relational information, i.e. document relationships and concept relationships. While exploiting the document network significantly improves topic coherence, the introduction of concepts and their relationships does not influence the results both quantitatively and qualitatively.",38940792,https://www.aclweb.org/anthology/2020.insights-1.5 +WS-4,WS-4.1175,Filtering before Iteratively Referring for Knowledge-Grounded Response Selection in Retrieval-Based Chatbots,Jia-Chen Gu,"The challenges of building knowledge-grounded retrieval-based chatbots lie in how to ground a conversation on its background knowledge and how to match response candidates with both context and knowledge simultaneously. This paper proposes a method named Filtering before Iteratively REferring (FIRE) for this task. In this method, a context filter and a knowledge filter are first built, which derive knowledge-aware context representations and context-aware knowledge representations respectively by global and bidirectional attention. Besides, the entries irrelevant to the conversation are discarded by the knowledge filter. After that, iteratively referring is performed between context and response representations as well as between knowledge and response representations, in order to collect deep matching features for scoring response candidates. Experimental results show that FIRE outperforms previous methods by margins larger than 2.8% and 4.1% on the PERSONA-CHAT dataset with original and revised personas respectively, and margins larger than 3.1% on the CMU_DoG dataset in terms of top-1 accuracy. We also show that FIRE is more interpretable by visualizing the knowledge grounding process.",38940705,https://www.aclweb.org/anthology/2020.findings-emnlp.127 +WS-4,WS-4.1735,TSDG: Content-aware Neural Response Generation with Two-stage Decoding Process,Junsheng Kong|Zhicheng Zhong|Yi Cai|Xin Wu|Da Ren,"Neural response generative models have achieved remarkable progress in recent years but tend to yield irrelevant and uninformative responses. One of the reasons is that encoder-decoder based models always use a single decoder to generate a complete response at a stroke. This tends to generate high-frequency function words with less semantic information rather than low-frequency content words with more semantic information. To address this issue, we propose a content-aware model with two-stage decoding process named Two-stage Dialogue Generation (TSDG). We separate the decoding process of content words and function words so that content words can be generated independently without the interference of function words. Experimental results on two datasets indicate that our model significantly outperforms several competitive generative models in terms of automatic and human evaluation.",38940706,https://www.aclweb.org/anthology/2020.findings-emnlp.192 +WS-4,WS-4.1761-ws4,ConveRT: Efficient and Accurate Conversational Representations from Transformers,Ivan Vulić,"General-purpose pretrained sentence encoders such as BERT are not ideal for real-world conversational AI applications; they are computationally heavy, slow, and expensive to train. We propose ConveRT (Conversational Representations from Transformers), a pretraining framework for conversational tasks satisfying all the following requirements: it is effective, affordable, and quick to train. We pretrain using a retrieval-based response selection task, effectively leveraging quantization and subword-level parameterization in the dual encoder to build a lightweight memory- and energy-efficient model. We show that ConveRT achieves state-of-the-art performance across widely established response selection tasks. We also demonstrate that the use of extended dialog history as context yields further performance gains. Finally, we show that pretrained representations from the proposed encoder can be transferred to the intent classification task, yielding strong results across three diverse data sets. ConveRT trains substantially faster than standard sentence encoders or previous state-of-the-art dual encoders. With its reduced size and superior performance, we believe this model promises wider portability and scalability for Conversational AI applications.",38940707,https://www.aclweb.org/anthology/2020.findings-emnlp.196 +WS-4,WS-4.1947,Learning Improvised Chatbots from Adversarial Modifications of Natural Language Feedback,Makesh Narsimhan Sreedhar|Kun Ni|Siva Reddy,"The ubiquitous nature of dialogue systems and their interaction with users generate an enormous amount of data. Can we improve chatbots using this data? A self-feeding chatbot improves itself by asking natural language feedback when a user is dissatisfied with its response and uses this feedback as an additional training sample. However, user feedback in most cases contains extraneous sequences hindering their usefulness as a training sample. In this work, we propose a generative adversarial model that converts noisy feedback into a plausible natural response in a conversation. The generator’s goal is to convert the feedback into a response that answers the user’s previous utterance and to fool the discriminator which distinguishes feedback from natural responses. We show that augmenting original training data with these modified feedback responses improves the original chatbot performance from 69.94%to 75.96% in ranking correct responses on the PERSONACHATdataset, a large improvement given that the original model is already trained on 131k samples.",38940708,https://www.aclweb.org/anthology/2020.findings-emnlp.221 +WS-4,WS-4.2041,Toward Stance-based Personas for Opinionated Dialogues,Thomas Scialom|Serra Sinem Tekiroglu|Jacopo Staiano|Marco Guerini,"In the context of chit-chat dialogues it has been shown that endowing systems with a persona profile is important to produce more coherent and meaningful conversations. Still, the representation of such personas has thus far been limited to a fact-based representation (e.g. “I have two cats.”). We argue that these representations remain superficial w.r.t. the complexity of human personality. In this work, we propose to make a step forward and investigate stance-based persona, trying to grasp more profound characteristics, such as opinions, values, and beliefs to drive language generation. To this end, we introduce a novel dataset allowing to explore different stance-based persona representations and their impact on claim generation, showing that they are able to grasp abstract and profound aspects of the author persona.",38940704,https://www.aclweb.org/anthology/2020.findings-emnlp.238 +WS-4,WS-4.2889,Joint Turn and Dialogue level User Satisfaction Estimation on Multi-Domain Conversations,Praveen Kumar Bodigutla|Aditya Tiwari|Josep Vallas Vargas|Lazaros Polymenakos|Spyros Matsoukas,"Dialogue level quality estimation is vital for optimizing data driven dialogue management. Current automated methods to estimate turn and dialogue level user satisfaction employ hand-crafted features and rely on complex annotation schemes, which reduce the generalizability of the trained models. We propose a novel user satisfaction estimation approach which minimizes an adaptive multi-task loss function in order to jointly predict turn-level Response Quality labels provided by experts and explicit dialogue-level ratings provided by end users. The proposed BiLSTM based deep neural net model automatically weighs each turn’s contribution towards the estimated dialogue-level rating, implicitly encodes temporal dependencies, and removes the need to hand-craft features. On dialogues sampled from 28 Alexa domains, two dialogue systems and three user groups, the joint dialogue-level satisfaction estimation model achieved up to an absolute 27% (0.43 -> 0.70) and 7% (0.63 -> 0.70) improvement in linear correlation performance over baseline deep neural net and benchmark Gradient boosting regression models, respectively.",38940709,https://www.aclweb.org/anthology/2020.findings-emnlp.347 +WS-4,WS-4.2957,Making Information Seeking Easier: An Improved Pipeline for Conversational Search,Vaibhav Kumar|Jamie Callan,"This paper presents a highly effective pipeline for passage retrieval in a conversational search setting. The pipeline comprises of two components: Conversational Term Selection (CTS) and Multi-View Reranking (MVR). CTS is responsible for performing the first-stage of passage retrieval. Given an input question, it uses a BERT-based classifier (trained with weak supervision) to de-contextualize the input by selecting relevant terms from the dialog history. Using the question and the selected terms, it issues a query to a search engine to perform the first-stage of passage retrieval. On the other hand, MVR is responsible for contextualized passage reranking. It first constructs multiple views of the information need embedded within an input question. The views are based on the dialog history and the top documents obtained in the first-stage of retrieval. It then uses each view to rerank passages using BERT (fine-tuned for passage ranking). Finally, MVR performs a fusion over the rankings produced by the individual views. Experiments show that the above combination improves first-state retrieval as well as the overall accuracy in a reranking pipeline. On the key metric of NDCG@3, the proposed combination achieves a relative performance improvement of 14.8% over the state-of-the-art baseline and is also able to surpass the Oracle.",38940710,https://www.aclweb.org/anthology/2020.findings-emnlp.354 +WS-4,WS-4.3361,SMRT Chatbots: Improving Non-Task-Oriented Dialog with Simulated Multi-Reference Training,Huda Khayrallah|João Sedoc,"Non-task-oriented dialog models suffer from poor quality and non-diverse responses. To overcome limited conversational data, we apply Simulated Multiple Reference Training (SMRT; Khayrallah et al., 2020), and use a paraphraser to simulate multiple responses per training prompt. We find SMRT improves over a strong Transformer baseline as measured by human and automatic quality scores and lexical diversity. We also find SMRT is comparable to pretraining in human evaluation quality, and outperforms pretraining on automatic quality and lexical diversity, without requiring related-domain dialog data.",38940711,https://www.aclweb.org/anthology/2020.findings-emnlp.403 WS-4,WS-4.paper1,TBA,Tba,,38940061, -WS-4,WS-4.paper2,A Wrong Answer or a Wrong Question? An Intricate Relationship between Question Reformulation and Answer Selection in Conversational Question Answering,Svitlana Vakulenko|Shayne Longpre|Zhucheng Tu|Raviteja Anantha,,38940062,https://www.aclweb.org/anthology/2020.scai-1.2 -WS-4,WS-4.paper3,Semantically Driven Sentence Fusion: Modeling and Evaluation,Eyal Ben-David,,38940063,https://www.aclweb.org/anthology/2020.findings-emnlp.135 +WS-4,WS-4.paper2,A Wrong Answer or a Wrong Question? An Intricate Relationship between Question Reformulation and Answer Selection in Conversational Question Answering,Svitlana Vakulenko|Shayne Longpre|Zhucheng Tu|Raviteja Anantha,"The dependency between an adequate question formulation and correct answer selection is a very intriguing but still underexplored area. In this paper, we show that question rewriting (QR) of the conversational context allows to shed more light on this phenomenon and also use it to evaluate robustness of different answer selection approaches. We introduce a simple framework that enables an automated analysis of the conversational question answering (QA) performance using question rewrites, and present the results of this analysis on the TREC CAsT and QuAC (CANARD) datasets. Our experiments uncover sensitivity to question formulation of the popular state-of-the-art question answering approaches. Our results demonstrate that the reading comprehension model is insensitive to question formulation, while the passage ranking changes dramatically with a little variation in the input question. The benefit of QR is that it allows us to pinpoint and group such cases automatically. We show how to use this methodology to verify whether QA models are really learning the task or just finding shortcuts in the dataset, and better understand the frequent types of error they make.",38940062,https://www.aclweb.org/anthology/2020.scai-1.2 +WS-4,WS-4.paper3,Semantically Driven Sentence Fusion: Modeling and Evaluation,Eyal Ben-David,"Sentence fusion is the task of joining related sentences into coherent text. Current training and evaluation schemes for this task are based on single reference ground-truths and do not account for valid fusion variants. We show that this hinders models from robustly capturing the semantic relationship between input sentences. To alleviate this, we present an approach in which ground-truth solutions are automatically expanded into multiple references via curated equivalence classes of connective phrases. We apply this method to a large-scale dataset and use the augmented dataset for both model training and evaluation. To improve the learning of semantic representation using multiple references, we enrich the model with auxiliary discourse classification tasks under a multi-tasking framework. Our experiments highlight the improvements of our approach over state-of-the-art models.",38940063,https://www.aclweb.org/anthology/2020.findings-emnlp.135 WS-4,WS-4.paper4,TBA,Tba,,38940064, WS-4,WS-4.paper5,TBA,Marco Guerini,,38940065, -WS-5,WS-5.16,Probabilistic Predictions of People Perusing: Evaluating Metrics of Language Model Performance for Psycholinguistic Modeling,Yiding Hao|Simon Mendelsohn|Rachel Sterneck|Randi Martinez|Robert Frank,,38939682,https://www.aclweb.org/anthology/2020.cmcl-1.10 -WS-5,WS-5.7,Images and Imagination: Automated Analysis of Priming Effects Related to Autism Spectrum Disorder and Developmental Language Disorder,Michaela Regneri|Diane King|Fahreen Walji|Olympia Palikara,,38939683,https://www.aclweb.org/anthology/2020.cmcl-1.2 -WS-6,WS-6.10,Learning Adaptive Language Interfaces through Decomposition,Siddharth Karamcheti|Dorsa Sadigh|Percy Liang,,38939456,https://www.aclweb.org/anthology/2020.intexsempar-1.4 -WS-6,WS-6.11,ColloQL: Robust Text-to-SQL Over Search Queries,Karthik Radhakrishnan|Arvind Srikantan|Xi Victoria Lin,,38939457,https://www.aclweb.org/anthology/2020.intexsempar-1.5 -WS-6,WS-6.12,Natural Language Response Generation from SQL with Generalization and Back-translation,Saptarashmi Bandyopadhyay|Tianyang Zhao,,38939458,https://www.aclweb.org/anthology/2020.intexsempar-1.6 +WS-5,WS-5.16,Probabilistic Predictions of People Perusing: Evaluating Metrics of Language Model Performance for Psycholinguistic Modeling,Yiding Hao|Simon Mendelsohn|Rachel Sterneck|Randi Martinez|Robert Frank,"By positing a relationship between naturalistic reading times and information-theoretic surprisal, surprisal theory (Hale, 2001; Levy, 2008) provides a natural interface between language models and psycholinguistic models. This paper re-evaluates a claim due to Goodkind and Bicknell (2018) that a language model’s ability to model reading times is a linear function of its perplexity. By extending Goodkind and Bicknell’s analysis to modern neural architectures, we show that the proposed relation does not always hold for Long Short-Term Memory networks, Transformers, and pre-trained models. We introduce an alternate measure of language modeling performance called predictability norm correlation based on Cloze probabilities measured from human subjects. Our new metric yields a more robust relationship between language model quality and psycholinguistic modeling performance that allows for comparison between models with different training configurations.",38939682,https://www.aclweb.org/anthology/2020.cmcl-1.10 +WS-5,WS-5.7,Images and Imagination: Automated Analysis of Priming Effects Related to Autism Spectrum Disorder and Developmental Language Disorder,Michaela Regneri|Diane King|Fahreen Walji|Olympia Palikara,"Different aspects of language processing have been shown to be sensitive to priming but the findings of studies examining priming effects in adolescents with Autism Spectrum Disorder (ASD) and Developmental Language Disorder (DLD) have been inconclusive. We present a study analysing visual and implicit semantic priming in adolescents with ASD and DLD. Based on a dataset of fictional and script-like narratives, we evaluate how often and how extensively, content of two different priming sources is used by the participants. The first priming source was visual, consisting of images shown to the participants to assist them with their storytelling. The second priming source originated from commonsense knowledge, using crowdsourced data containing prototypical script elements. Our results show that individuals with ASD are less sensitive to both types of priming, but show typical usage of primed cues when they use them at all. In contrast, children with DLD show mostly average priming sensitivity, but exhibit an over-proportional use of the priming cues.",38939683,https://www.aclweb.org/anthology/2020.cmcl-1.2 +WS-6,WS-6.10,Learning Adaptive Language Interfaces through Decomposition,Siddharth Karamcheti|Dorsa Sadigh|Percy Liang,"Our goal is to create an interactive natural language interface that efficiently and reliably learns from users to complete tasks in simulated robotics settings. We introduce a neural semantic parsing system that learns new high-level abstractions through decomposition: users interactively teach the system by breaking down high-level utterances describing novel behavior into low-level steps that it can understand. Unfortunately, existing methods either rely on grammars which parse sentences with limited flexibility, or neural sequence-to-sequence models that do not learn efficiently or reliably from individual examples. Our approach bridges this gap, demonstrating the flexibility of modern neural systems, as well as the one-shot reliable generalization of grammar-based methods. Our crowdsourced interactive experiments suggest that over time, users complete complex tasks more efficiently while using our system by leveraging what they just taught. At the same time, getting users to trust the system enough to be incentivized to teach high-level utterances is still an ongoing challenge. We end with a discussion of some of the obstacles we need to overcome to fully realize the potential of the interactive paradigm.",38939456,https://www.aclweb.org/anthology/2020.intexsempar-1.4 +WS-6,WS-6.11,ColloQL: Robust Text-to-SQL Over Search Queries,Karthik Radhakrishnan|Arvind Srikantan|Xi Victoria Lin,"Translating natural language utterances to executable queries is a helpful technique in making the vast amount of data stored in relational databases accessible to a wider range of non-tech-savvy end users. Prior work in this area has largely focused on textual input that is linguistically correct and semantically unambiguous. However, real-world user queries are often succinct, colloquial, and noisy, resembling the input of a search engine. In this work, we introduce data augmentation techniques and a sampling-based content-aware BERT model (ColloQL) to achieve robust text-to-SQL modeling over natural language search (NLS) questions. Due to the lack of evaluation data, we curate a new dataset of NLS questions and demonstrate the efficacy of our approach. ColloQL’s superior performance extends to well-formed text, achieving an 84.9% (logical) and 90.7% (execution) accuracy on the WikiSQL dataset, making it, to the best of our knowledge, the highest performing model that does not use execution guided decoding.",38939457,https://www.aclweb.org/anthology/2020.intexsempar-1.5 +WS-6,WS-6.12,Natural Language Response Generation from SQL with Generalization and Back-translation,Saptarashmi Bandyopadhyay|Tianyang Zhao,"Generation of natural language responses to the queries of structured language like SQL is very challenging as it requires generalization to new domains and the ability to answer ambiguous queries among other issues. We have participated in the CoSQL shared task organized in the IntEx-SemPar workshop at EMNLP 2020. We have trained a number of Neural Machine Translation (NMT) models to efficiently generate the natural language responses from SQL. Our shuffled back-translation model has led to a BLEU score of 7.47 on the unknown test dataset. In this paper, we will discuss our methodologies to approach the problem and future directions to improve the quality of the generated natural language responses.",38939458,https://www.aclweb.org/anthology/2020.intexsempar-1.6 WS-6,WS-6.13,GRAPPA: Grammar-Augmented Pre-Training for Table Semantic Parsing,Tao Yu|Chien-Sheng Wu|Xi Victoria Lin|Bailin Wang|Yi Chern Tan|Xinyi Yang|Dragomir Radev|Richard Socher|Caiming Xiong,,38939459, WS-6,WS-6.14,Re-thinking Open-domain Semantic Parsing,Yu Gu|Sue Kase|Michelle Vanni|Brian Sadler|Percy Liang|Xifeng Yan|Yu Su,,38939460, WS-6,WS-6.15,Did You Ask a Good Question? A Cross-Domain Question Intention Classification Benchmark for Text-to-SQL,Yusen Zhang|Xiangyu Dong|Shuaichen Chang|Tao Yu|Peng Shi|Rui Zhang,,38939461, -WS-6,WS-6.7,QA2Explanation: Generating and Evaluating Explanations for Question Answering Systems over Knowledge Graph,Saeedeh Shekarpour|Abhishek Nadgeri|Kuldeep Singh,,38939453,https://www.aclweb.org/anthology/2020.intexsempar-1.1 -WS-6,WS-6.8,Uncertainty and Traffic-Aware Active Learning for Semantic Parsing,Priyanka Sen|Emine Yilmaz,,38939454,https://www.aclweb.org/anthology/2020.intexsempar-1.2 -WS-6,WS-6.9,Improving Sequence-to-Sequence Semantic Parser for Task Oriented Dialog,Chaoting Xuan,,38939455,https://www.aclweb.org/anthology/2020.intexsempar-1.3 -WS-7,WS-7.14,Improved Local Citation Recommendation Based on Context Enhanced with Global Information,Zoran Medić|Jan Snajder,,38940720,https://www.aclweb.org/anthology/2020.sdp-1.11 -WS-7,WS-7.1457,Will this Idea Spread Beyond Academia? Understanding Knowledge Transfer of Scientific Concepts across Text Corpora,Hancheng Cao|Mengjie Cheng|Zhepeng Cen|Daniel A. McFarland|Xiang Ren,,38940721,https://www.aclweb.org/anthology/2020.findings-emnlp.158 -WS-7,WS-7.15shared,"CIST@CL-SciSumm 2020, LongSumm 2020: Automatic Scientific Document Summarization",Lei Li|Yang Xie|Wei Liu|Yinan Liu|Yafei Jiang|Siya Qi|Xingyuan Li,,38940743,https://www.aclweb.org/anthology/2020.sdp-1.25 -WS-7,WS-7.17,SciWING– A Software Toolkit for Scientific Document Processing,Abhinav Ramesh Kashyap|Min-Yen Kan,,38940731,https://www.aclweb.org/anthology/2020.sdp-1.13 -WS-7,WS-7.1728,"MedICaT: A Dataset of Medical Images, Captions, and Textual References",Sanjay Subramanian|Lucy Lu Wang|Sachin Mehta|Ben Bogin|Madeleine Van Zuylen|Sravanthi Parasar|Sameer Singh|Matt Gardner|Hannaneh Hajishirzi,,38940723,https://www.aclweb.org/anthology/2020.findings-emnlp.191 -WS-7,WS-7.18,NLP-PINGAN-TECH @ CL-SciSumm 2020,Ling Chai|Guizhen Fu|Yuan Ni,,38941223,https://www.aclweb.org/anthology/2020.sdp-1.26 -WS-7,WS-7.20shared,Dimsum @LaySumm 20,Tiezheng Yu|Dan Su|Wenliang Dai|Pascale Fung,,38940741,https://www.aclweb.org/anthology/2020.sdp-1.35 -WS-7,WS-7.21,Multi-task Peer-Review Score Prediction,Jiyi Li|Ayaka Sato|Kazuya Shimura|Fumiyo Fukumoto,,38940727,https://www.aclweb.org/anthology/2020.sdp-1.14 -WS-7,WS-7.22,ERLKG: Entity Representation Learning and Knowledge Graph based association analysis of COVID-19 through mining of unstructured biomedical corpora,Sayantan Basu|Sinchani Chakraborty|Atif Hassan|Sana Siddique|Ashish Anand,,38940725,https://www.aclweb.org/anthology/2020.sdp-1.15 -WS-7,WS-7.24,Towards Grounding of Formulae,Takuto Asakura|ré Greiner-Petter|Akiko Aizawa|Yusuke Miyao,,38940733,https://www.aclweb.org/anthology/2020.sdp-1.16 -WS-7,WS-7.2502,Literature Retrieval for Precision Medicine with Neural Matching and Faceted Summarization,Jiho Noh|Ramakanth Kavuluru,,38940722,https://www.aclweb.org/anthology/2020.findings-emnlp.304 -WS-7,WS-7.26,Learning CNF Blocking for Large-scale Author Name Disambiguation,Kunho Kim|Athar Sefid|C Lee Giles,,38940717,https://www.aclweb.org/anthology/2020.sdp-1.8 -WS-7,WS-7.27,SChuBERT: Scholarly Document Chunks with BERT-encoding boost Citation Count Prediction,Thomas Van Dongen|Gideon Maillette De Buy Wenniger|Lambert Schomaker,,38940730,https://www.aclweb.org/anthology/2020.sdp-1.17 -WS-7,WS-7.29,Structure-Tags Improve Text Classification for Scholarly Document Quality Prediction,Gideon Maillette De Buy Wenniger|Thomas Van Dongen|Eleri Aedmaa|Herbert Teun Kruitbosch|Edwin A. Valentijn|Lambert Schomaker,,38940732,https://www.aclweb.org/anthology/2020.sdp-1.18 -WS-7,WS-7.30,Cydex: Neural Search Infrastructure for the Scholarly Literature,Shane Ding|Edwin Zhang|Jimmy Lin,,38940734,https://www.aclweb.org/anthology/2020.sdp-1.19 -WS-7,WS-7.32,A Smart System to Generate and Validate Question Answer Pairs for COVID-19 Literature,Rohan Bhambhoria|Luna Feng|Dawn Sepehr|John Chen|Conner Cowling|Sedef Kocak|Elham Dolatabadi,,38940713,https://www.aclweb.org/anthology/2020.sdp-1.4 -WS-7,WS-7.35,On the Use of Web Search to Improve Scientific Collections,Krutarth Patel|Cornelia Caragea|Sujatha Das Gollapalli,,38940728,https://www.aclweb.org/anthology/2020.sdp-1.20 -WS-7,WS-7.36,Scaling Systematic Literature Reviews with Machine Learning Pipelines,Seraphina Goldfarb-Tarrant|Alexander Robertson|Louise Donnison|Jasmina Lazic|Theodora Tsouloufi|Karen Smyth,,38940729,https://www.aclweb.org/anthology/2020.sdp-1.21 -WS-7,WS-7.37shared,Using Pre-Trained Transformer for a better Lay Summarization,Seungwon Kim,,38940740,https://www.aclweb.org/anthology/2020.sdp-1.38 -WS-7,WS-7.39,Acknowledgement Entity Recognition in CORD-19 Papers,Jian Wu|Pei Wang|Xin Wei|Sarah Rajtmajer|C Lee Giles|Christopher Griffin,,38940712,https://www.aclweb.org/anthology/2020.sdp-1.3 -WS-7,WS-7.40,DeepPaperComposer: A Simple Solution for Training Data Preparation for Parsing Research Papers,Meng Ling|Jian Chen,,38940719,https://www.aclweb.org/anthology/2020.sdp-1.10 -WS-7,WS-7.42,"Document-Level Definition Detection in Scholarly Documents: Existing Models, Error Analyses, and Future Directions",Dongyeop Kang|Rew Head|Risham Sidhu|Kyle Lo|Daniel Weld|Marti A. Hearst,,38940724,https://www.aclweb.org/anthology/2020.sdp-1.22 -WS-7,WS-7.43shared,"IIITBH-IITP@CL-SciSumm20, CL-LaySumm20, LongSumm20",Saichethan Miriyala Reddy|Naveen Saini|Sriparna Saha|Pushpak Bhattacharyya,,38940739,https://www.aclweb.org/anthology/2020.sdp-1.27 -WS-7,WS-7.44,Reconstructing Manual Information Extraction with DB-to-Document Backprojection: Experiments in the Life Science Domain,Mark-Christoph Muller|Sucheta Ghosh|Maja Rey|Ulrike Wittig|Wolfgang Muller|Michael Strube,,38940718,https://www.aclweb.org/anthology/2020.sdp-1.9 -WS-7,WS-7.45,"AUTH @ CLSciSumm 20, LaySumm 20, LongSumm 20",Alexios Gidiotis|Stefanos Stefanidis|Grigorios Tsoumakas,,38941222,https://www.aclweb.org/anthology/2020.sdp-1.28 -WS-7,WS-7.48_2shared,"SUMMAFORMERS @ LAYSUMM 20, LONGSUMM 20",Sayar Ghosh Roy|Nikhil Pinnaparaju|Risubh Jain|Manish Gupta|Vasudeva Varma,,38940742,https://www.aclweb.org/anthology/2020.sdp-1.39 +WS-6,WS-6.7,QA2Explanation: Generating and Evaluating Explanations for Question Answering Systems over Knowledge Graph,Saeedeh Shekarpour|Abhishek Nadgeri|Kuldeep Singh,"In the era of Big Knowledge Graphs, Question Answering (QA) systems have reached a milestone in their performance and feasibility. However, their applicability, particularly in specific domains such as the biomedical domain, has not gained wide acceptance due to their “black box” nature, which hinders transparency, fairness, and accountability of QA systems. Therefore, users are unable to understand how and why particular questions have been answered, whereas some others fail. To address this challenge, in this paper, we develop an automatic approach for generating explanations during various stages of a pipeline-based QA system. Our approach is a supervised and automatic approach which considers three classes (i.e., success, no answer, and wrong answer) for annotating the output of involved QA components. Upon our prediction, a template explanation is chosen and integrated into the output of the corresponding component. To measure the effectiveness of the approach, we conducted a user survey as to how non-expert users perceive our generated explanations. The results of our study show a significant increase in the four dimensions of the human factor from the Human-computer interaction community.",38939453,https://www.aclweb.org/anthology/2020.intexsempar-1.1 +WS-6,WS-6.8,Uncertainty and Traffic-Aware Active Learning for Semantic Parsing,Priyanka Sen|Emine Yilmaz,"Collecting training data for semantic parsing is a time-consuming and expensive task. As a result, there is growing interest in industry to reduce the number of annotations required to train a semantic parser, both to cut down on costs and to limit customer data handled by annotators. In this paper, we propose uncertainty and traffic-aware active learning, a novel active learning method that uses model confidence and utterance frequencies from customer traffic to select utterances for annotation. We show that our method significantly outperforms baselines on an internal customer dataset and the Facebook Task Oriented Parsing (TOP) dataset. On our internal dataset, our method achieves the same accuracy as random sampling with 2,000 fewer annotations.",38939454,https://www.aclweb.org/anthology/2020.intexsempar-1.2 +WS-6,WS-6.9,Improving Sequence-to-Sequence Semantic Parser for Task Oriented Dialog,Chaoting Xuan,"Task Oriented Parsing (TOP) attempts to map utterances to compositional requests, including multiple intents and their slots. Previous work focus on a tree-based hierarchical meaning representation, and applying constituency parsing techniques to address TOP. In this paper, we propose a new format of meaning representation that is more compact and amenable to sequence-to-sequence (seq-to-seq) models. A simple copy-augmented seq-to-seq parser is built and evaluated over a public TOP dataset, resulting in 3.44% improvement over prior best seq-to-seq parser (exact match accuracy), which is also comparable to constituency parsers’ performance.",38939455,https://www.aclweb.org/anthology/2020.intexsempar-1.3 +WS-7,WS-7.14,Improved Local Citation Recommendation Based on Context Enhanced with Global Information,Zoran Medić|Jan Snajder,"Local citation recommendation aims at finding articles relevant for given citation context. While most previous approaches represent context using solely text surrounding the citation, we propose enhancing context representation with global information. Specifically, we include citing article’s title and abstract into context representation. We evaluate our model on datasets with different citation context sizes and demonstrate improvements with globally-enhanced context representations when citation contexts are smaller.",38940720,https://www.aclweb.org/anthology/2020.sdp-1.11 +WS-7,WS-7.1457,Will this Idea Spread Beyond Academia? Understanding Knowledge Transfer of Scientific Concepts across Text Corpora,Hancheng Cao|Mengjie Cheng|Zhepeng Cen|Daniel A. McFarland|Xiang Ren,"What kind of basic research ideas are more likely to get applied in practice? There is a long line of research investigating patterns of knowledge transfer, but it generally focuses on documents as the unit of analysis and follow their transfer into practice for a specific scientific domain. Here we study translational research at the level of scientific concepts for all scientific fields. We do this through text mining and predictive modeling using three corpora: 38.6 million paper abstracts, 4 million patent documents, and 0.28 million clinical trials. We extract scientific concepts (i.e., phrases) from corpora as instantiations of “research ideas”, create concept-level features as motivated by literature, and then follow the trajectories of over 450,000 new concepts (emerged from 1995-2014) to identify factors that lead only a small proportion of these ideas to be used in inventions and drug trials. Results from our analysis suggest several mechanisms that distinguish which scientific concept will be adopted in practice, and which will not. We also demonstrate that our derived features can be used to explain and predict knowledge transfer with high accuracy. Our work provides greater understanding of knowledge transfer for researchers, practitioners, and government agencies interested in encouraging translational research.",38940721,https://www.aclweb.org/anthology/2020.findings-emnlp.158 +WS-7,WS-7.15shared,"CIST@CL-SciSumm 2020, LongSumm 2020: Automatic Scientific Document Summarization",Lei Li|Yang Xie|Wei Liu|Yinan Liu|Yafei Jiang|Siya Qi|Xingyuan Li,"Our system participates in two shared tasks, CL-SciSumm 2020 and LongSumm 2020. In the CL-SciSumm shared task, based on our previous work, we apply more machine learning methods on position features and content features for facet classification in Task1B. And GCN is introduced in Task2 to perform extractive summarization. In the LongSumm shared task, we integrate both the extractive and abstractive summarization ways. Three methods were tested which are T5 Fine-tuning, DPPs Sampling, and GRU-GCN/GAT.",38940743,https://www.aclweb.org/anthology/2020.sdp-1.25 +WS-7,WS-7.17,SciWING– A Software Toolkit for Scientific Document Processing,Abhinav Ramesh Kashyap|Min-Yen Kan,"We introduce SciWING, an open-source soft-ware toolkit which provides access to state-of-the-art pre-trained models for scientific document processing (SDP) tasks, such as citation string parsing, logical structure recovery and citation intent classification. Compared to other toolkits, SciWING follows a full neural pipeline and provides a Python inter-face for SDP. When needed, SciWING provides fine-grained control for rapid experimentation with different models by swapping and stacking different modules. Transfer learning from general and scientific documents specific pre-trained transformers (i.e., BERT, SciBERT, etc.) can be performed. SciWING incorporates ready-to-use web and terminal-based applications and demonstrations to aid adoption and development. The toolkit is available from http://sciwing.io and the demos are available at http://rebrand.ly/sciwing-demo.",38940731,https://www.aclweb.org/anthology/2020.sdp-1.13 +WS-7,WS-7.1728,"MedICaT: A Dataset of Medical Images, Captions, and Textual References",Sanjay Subramanian|Lucy Lu Wang|Sachin Mehta|Ben Bogin|Madeleine Van Zuylen|Sravanthi Parasar|Sameer Singh|Matt Gardner|Hannaneh Hajishirzi,"Understanding the relationship between figures and text is key to scientific document understanding. Medical figures in particular are quite complex, often consisting of several subfigures (75% of figures in our dataset), with detailed text describing their content. Previous work studying figures in scientific papers focused on classifying figure content rather than understanding how images relate to the text. To address challenges in figure retrieval and figure-to-text alignment, we introduce MedICaT, a dataset of medical images in context. MedICaT consists of 217K images from 131K open access biomedical papers, and includes captions, inline references for 74% of figures, and manually annotated subfigures and subcaptions for a subset of figures. Using MedICaT, we introduce the task of subfigure to subcaption alignment in compound figures and demonstrate the utility of inline references in image-text matching. Our data and code can be accessed at https://github.com/allenai/medicat.",38940723,https://www.aclweb.org/anthology/2020.findings-emnlp.191 +WS-7,WS-7.18,NLP-PINGAN-TECH @ CL-SciSumm 2020,Ling Chai|Guizhen Fu|Yuan Ni,"We focus on systems for TASK1 (TASK 1A and TASK 1B) of CL-SciSumm Shared Task 2020 in this paper. Task 1A is regarded as a binary classification task of sentence pairs. The strategies of domain-specific embedding and special tokens based on language models are proposed. Fusion of contextualized embedding and extra information is further explored in this article. We leverage Sembert to capture the structured semantic information. The joint of BERT-based model and classifiers without neural networks is also exploited. For the Task 1B, a language model with different weights for classes is fine-tuned to accomplish a multi-label classification task. The results show that extra information can improve the identification of cited text spans. The end-to-end trained models outperform models trained with two stages, and the averaged prediction of multi-models is more accurate than an individual one.",38941223,https://www.aclweb.org/anthology/2020.sdp-1.26 +WS-7,WS-7.20shared,Dimsum @LaySumm 20,Tiezheng Yu|Dan Su|Wenliang Dai|Pascale Fung,"Lay summarization aims to generate lay summaries of scientific papers automatically. It is an essential task that can increase the relevance of science for all of society. In this paper, we build a lay summary generation system based on BART model. We leverage sentence labels as extra supervision signals to improve the performance of lay summarization. In the CL-LaySumm 2020 shared task, our model achieves 46.00 Rouge1-F1 score.",38940741,https://www.aclweb.org/anthology/2020.sdp-1.35 +WS-7,WS-7.21,Multi-task Peer-Review Score Prediction,Jiyi Li|Ayaka Sato|Kazuya Shimura|Fumiyo Fukumoto,"Automatic prediction on the peer-review aspect scores of academic papers can be a useful assistant tool for both reviewers and authors. To handle the small size of published datasets on the target aspect of scores, we propose a multi-task approach to leverage additional information from other aspects of scores for improving the performance of the target. Because one of the problems of building multi-task models is how to select the proper resources of auxiliary tasks and how to select the proper shared structures. We propose a multi-task shared structure encoding approach which automatically selects good shared network structures as well as good auxiliary resources. The experiments based on peer-review datasets show that our approach is effective and has better performance on the target scores than the single-task method and naive multi-task methods.",38940727,https://www.aclweb.org/anthology/2020.sdp-1.14 +WS-7,WS-7.22,ERLKG: Entity Representation Learning and Knowledge Graph based association analysis of COVID-19 through mining of unstructured biomedical corpora,Sayantan Basu|Sinchani Chakraborty|Atif Hassan|Sana Siddique|Ashish Anand,"We introduce a generic, human-out-of-the-loop pipeline, ERLKG, to perform rapid association analysis of any biomedical entity with other existing entities from a corpora of the same domain. Our pipeline consists of a Knowledge Graph (KG) created from the Open Source CORD-19 dataset by fully automating the procedure of information extraction using SciBERT. The best latent entity representations are then found by benchnmarking different KG embedding techniques on the task of link prediction using a Graph Convolution Network Auto Encoder (GCN-AE). We demonstrate the utility of ERLKG with respect to COVID-19 through multiple qualitative evaluations. Due to the lack of a gold standard, we propose a relatively large intrinsic evaluation dataset for COVID-19 and use it for validating the top two performing KG embedding techniques. We find TransD to be the best performing KG embedding technique with Pearson and Spearman correlation scores of 0.4348 and 0.4570 respectively. We demonstrate that a considerable number of ERLKG’s top protein, chemical and disease predictions are currently in consideration for COVID-19 related research.",38940725,https://www.aclweb.org/anthology/2020.sdp-1.15 +WS-7,WS-7.24,Towards Grounding of Formulae,Takuto Asakura|ré Greiner-Petter|Akiko Aizawa|Yusuke Miyao,"A large amount of scientific knowledge is represented within mixed forms of natural language texts and mathematical formulae. Therefore, a collaboration of natural language processing and formula analyses, so-called mathematical language processing, is necessary to enable computers to understand and retrieve information from the documents. However, as we will show in this project, a mathematical notation can change its meaning even within the scope of a single paragraph. This flexibility makes it difficult to extract the exact meaning of a mathematical formula. In this project, we will propose a new task direction for grounding mathematical formulae. Particularly, we are addressing the widespread misconception of various research projects in mathematical information retrieval, which presume that mathematical notations have a fixed meaning within a single document. We manually annotated a long scientific paper to illustrate the task concept. Our high inter-annotator agreement shows that the task is well understood for humans. Our results indicate that it is worthwhile to grow the techniques for the proposed task to contribute to the further progress of mathematical language processing.",38940733,https://www.aclweb.org/anthology/2020.sdp-1.16 +WS-7,WS-7.2502,Literature Retrieval for Precision Medicine with Neural Matching and Faceted Summarization,Jiho Noh|Ramakanth Kavuluru,"Information retrieval (IR) for precision medicine (PM) often involves looking for multiple pieces of evidence that characterize a patient case. This typically includes at least the name of a condition and a genetic variation that applies to the patient. Other factors such as demographic attributes, comorbidities, and social determinants may also be pertinent. As such, the retrieval problem is often formulated as ad hoc search but with multiple facets (e.g., disease, mutation) that may need to be incorporated. In this paper, we present a document reranking approach that combines neural query-document matching and text summarization toward such retrieval scenarios. Our architecture builds on the basic BERT model with three specific components for reranking: (a). document-query matching (b). keyword extraction and (c). facet-conditioned abstractive summarization. The outcomes of (b) and (c) are used to essentially transform a candidate document into a concise summary that can be compared with the query at hand to compute a relevance score. Component (a) directly generates a matching score of a candidate document for a query. The full architecture benefits from the complementary potential of document-query matching and the novel document transformation approach based on summarization along PM facets. Evaluations using NIST’s TREC-PM track datasets (2017–2019) show that our model achieves state-of-the-art performance. To foster reproducibility, our code is made available here: https://github.com/bionlproc/text-summ-for-doc-retrieval.",38940722,https://www.aclweb.org/anthology/2020.findings-emnlp.304 +WS-7,WS-7.26,Learning CNF Blocking for Large-scale Author Name Disambiguation,Kunho Kim|Athar Sefid|C Lee Giles,"Author name disambiguation (AND) algorithms identify a unique author entity record from all similar or same publication records in scholarly or similar databases. Typically, a clustering method is used that requires calculation of similarities between each possible record pair. However, the total number of pairs grows quadratically with the size of the author database making such clustering difficult for millions of records. One remedy is a blocking function that reduces the number of pairwise similarity calculations. Here, we introduce a new way of learning blocking schemes by using a conjunctive normal form (CNF) in contrast to the disjunctive normal form (DNF). We demonstrate on PubMed author records that CNF blocking reduces more pairs while preserving high pairs completeness compared to the previous methods that use a DNF and that the computation time is significantly reduced. In addition, we also show how to ensure that the method produces disjoint blocks so that much of the AND algorithm can be efficiently paralleled. Our CNF blocking method is tested on the entire PubMed database of 80 million author mentions and efficiently removes 82.17% of all author record pairs in 10 minutes.",38940717,https://www.aclweb.org/anthology/2020.sdp-1.8 +WS-7,WS-7.27,SChuBERT: Scholarly Document Chunks with BERT-encoding boost Citation Count Prediction,Thomas Van Dongen|Gideon Maillette De Buy Wenniger|Lambert Schomaker,"Predicting the number of citations of scholarly documents is an upcoming task in scholarly document processing. Besides the intrinsic merit of this information, it also has a wider use as an imperfect proxy for quality which has the advantage of being cheaply available for large volumes of scholarly documents. Previous work has dealt with number of citations prediction with relatively small training data sets, or larger datasets but with short, incomplete input text. In this work we leverage the open access ACL Anthology collection in combination with the Semantic Scholar bibliometric database to create a large corpus of scholarly documents with associated citation information and we propose a new citation prediction model called SChuBERT. In our experiments we compare SChuBERT with several state-of-the-art citation prediction models and show that it outperforms previous methods by a large margin. We also show the merit of using more training data and longer input for number of citations prediction.",38940730,https://www.aclweb.org/anthology/2020.sdp-1.17 +WS-7,WS-7.29,Structure-Tags Improve Text Classification for Scholarly Document Quality Prediction,Gideon Maillette De Buy Wenniger|Thomas Van Dongen|Eleri Aedmaa|Herbert Teun Kruitbosch|Edwin A. Valentijn|Lambert Schomaker,"Training recurrent neural networks on long texts, in particular scholarly documents, causes problems for learning. While hierarchical attention networks (HANs) are effective in solving these problems, they still lose important information about the structure of the text. To tackle these problems, we propose the use of HANs combined with structure-tags which mark the role of sentences in the document. Adding tags to sentences, marking them as corresponding to title, abstract or main body text, yields improvements over the state-of-the-art for scholarly document quality prediction. The proposed system is applied to the task of accept/reject prediction on the PeerRead dataset and compared against a recent BiLSTM-based model and joint textual+visual model as well as against plain HANs. Compared to plain HANs, accuracy increases on all three domains.On the computation and language domain our new model works best overall, and increases accuracy 4.7% over the best literature result. We also obtain improvements when introducing the tags for prediction of the number of citations for 88k scientific publications that we compiled from the Allen AI S2ORC dataset. For our HAN-system with structure-tags we reach 28.5% explained variance, an improvement of 1.8% over our reimplementation of the BiLSTM-based model as well as 1.0% improvement over plain HANs.",38940732,https://www.aclweb.org/anthology/2020.sdp-1.18 +WS-7,WS-7.30,Cydex: Neural Search Infrastructure for the Scholarly Literature,Shane Ding|Edwin Zhang|Jimmy Lin,"Cydex is a platform that provides neural search infrastructure for domain-specific scholarly literature. The platform represents an abstraction of Covidex, our recently developed full-stack open-source search engine for the COVID-19 Open Research Dataset (CORD-19) from AI2. While Covidex takes advantage of the latest best practices for keyword search using the popular Lucene search library as well as state-of-the-art neural ranking models using T5, parts of the system were hard coded to only work with CORD-19. This paper describes our efforts to generalize Covidex into Cydex, which can be applied to scholarly literature in different domains. By decoupling corpus-specific configurations from the frontend implementation, we are able to demonstrate the generality of Cydex on two very different corpora: the ACL Anthology and a collection of hydrology abstracts. Our platform is entirely open source and available at cydex.ai.",38940734,https://www.aclweb.org/anthology/2020.sdp-1.19 +WS-7,WS-7.32,A Smart System to Generate and Validate Question Answer Pairs for COVID-19 Literature,Rohan Bhambhoria|Luna Feng|Dawn Sepehr|John Chen|Conner Cowling|Sedef Kocak|Elham Dolatabadi,"Automatically generating question answer (QA) pairs from the rapidly growing coronavirus-related literature is of great value to the medical community. Creating high quality QA pairs would allow researchers to build models to address scientific queries for answers which are not readily available in support of the ongoing fight against the pandemic. QA pair generation is, however, a very tedious and time consuming task requiring domain expertise for annotation and evaluation. In this paper we present our contribution in addressing some of the challenges of building a QA system without gold data. We first present a method to create QA pairs from a large semi-structured dataset through the use of transformer and rule-based models. Next, we propose a means of engaging subject matter experts (SMEs) for annotating the QA pairs through the usage of a web application. Finally, we demonstrate some experiments showcasing the effectiveness of leveraging active learning in designing a high performing model with a substantially lower annotation effort from the domain experts.",38940713,https://www.aclweb.org/anthology/2020.sdp-1.4 +WS-7,WS-7.35,On the Use of Web Search to Improve Scientific Collections,Krutarth Patel|Cornelia Caragea|Sujatha Das Gollapalli,"Despite the advancements in search engine features, ranking methods, technologies, and the availability of programmable APIs, current-day open-access digital libraries still rely on crawl-based approaches for acquiring their underlying document collections. In this paper, we propose a novel search-driven framework for acquiring documents for such scientific portals. Within our framework, publicly-available research paper titles and author names are used as queries to a Web search engine. We were able to obtain ~267,000 unique research papers through our fully-automated framework using ~76,000 queries, resulting in almost 200,000 more papers than the number of queries. Moreover, through a combination of title and author name search, we were able to recover 78% of the original searched titles.",38940728,https://www.aclweb.org/anthology/2020.sdp-1.20 +WS-7,WS-7.36,Scaling Systematic Literature Reviews with Machine Learning Pipelines,Seraphina Goldfarb-Tarrant|Alexander Robertson|Louise Donnison|Jasmina Lazic|Theodora Tsouloufi|Karen Smyth,"Systematic reviews, which entail the extraction of data from large numbers of scientific documents, are an ideal avenue for the application of machine learning. They are vital to many fields of science and philanthropy, but are very time-consuming and require experts. Yet the three main stages of a systematic review are easily done automatically: searching for documents can be done via APIs and scrapers, selection of relevant documents can be done via binary classification, and extraction of data can be done via sequence-labelling classification. Despite the promise of automation for this field, little research exists that examines the various ways to automate each of these tasks. We construct a pipeline that automates each of these aspects, and experiment with many human-time vs. system quality trade-offs. We test the ability of classifiers to work well on small amounts of data and to generalise to data from countries not represented in the training data. We test different types of data extraction with varying difficulty in annotation, and five different neural architectures to do the extraction. We find that we can get surprising accuracy and generalisability of the whole pipeline system with only 2 weeks of human-expert annotation, which is only 15% of the time it takes to do the whole review manually and can be repeated and extended to new data with no additional effort.",38940729,https://www.aclweb.org/anthology/2020.sdp-1.21 +WS-7,WS-7.37shared,Using Pre-Trained Transformer for a better Lay Summarization,Seungwon Kim,"In this paper, we tack lay summarization tasks, which aim to automatically produce lay summaries for scientific papers, to participate in the first CL-LaySumm 2020 in SDP workshop at EMNLP 2020. We present our approach of using Pre-training with Extracted Gap-sentences for Abstractive Summarization (PEGASUS; Zhang et al., 2019b) to produce the lay summary and combining those with the extractive summarization model using Bidirectional Encoder Representations from Transformers (BERT; Devlin et al., 2018) and readability metrics that measure the readability of the sentence to further improve the quality of the summary. Our model achieves a remarkable performance on ROUGE metrics, demonstrating the produced summary is more readable while it summarizes the main points of the document.",38940740,https://www.aclweb.org/anthology/2020.sdp-1.38 +WS-7,WS-7.39,Acknowledgement Entity Recognition in CORD-19 Papers,Jian Wu|Pei Wang|Xin Wei|Sarah Rajtmajer|C Lee Giles|Christopher Griffin,"Acknowledgements are ubiquitous in scholarly papers. Existing acknowledgement entity recognition methods assume all named entities are acknowledged. Here, we examine the nuances between acknowledged and named entities by analyzing sentence structure. We develop an acknowledgement extraction system, AckExtract based on open-source text mining software and evaluate our method using manually labeled data. AckExtract uses the PDF of a scholarly paper as input and outputs acknowledgement entities. Results show an overall performance of F_1=0.92. We built a supplementary database by linking CORD-19 papers with acknowledgement entities extracted by AckExtract including persons and organizations and find that only up to 50–60% of named entities are actually acknowledged. We further analyze chronological trends of acknowledgement entities in CORD-19 papers. All codes and labeled data are publicly available at https://github.com/lamps-lab/ackextract.",38940712,https://www.aclweb.org/anthology/2020.sdp-1.3 +WS-7,WS-7.40,DeepPaperComposer: A Simple Solution for Training Data Preparation for Parsing Research Papers,Meng Ling|Jian Chen,"We present DeepPaperComposer, a simple solution for preparing highly accurate (100%) training data without manual labeling to extract content from scholarly articles using convolutional neural networks (CNNs). We used our approach to generate data and trained CNNs to extract eight categories of both textual (titles, abstracts, headers, figure and table captions, and other texts) and non-textural content (figures and tables) from 30 years of IEEE VIS conference papers, of which a third were scanned bitmap PDFs. We curated this dataset and named it VISpaper-3K. We then showed our initial benchmark performance using VISpaper-3K over itself and CS-150 using YOLOv3 and Faster-RCNN. We open-source DeepPaperComposer of our training data generation and released the resulting annotation data VISpaper-3K to promote re-producible research.",38940719,https://www.aclweb.org/anthology/2020.sdp-1.10 +WS-7,WS-7.42,"Document-Level Definition Detection in Scholarly Documents: Existing Models, Error Analyses, and Future Directions",Dongyeop Kang|Rew Head|Risham Sidhu|Kyle Lo|Daniel Weld|Marti A. Hearst,"The task of definition detection is important for scholarly papers, because papers often make use of technical terminology that may be unfamiliar to readers. Despite prior work on definition detection, current approaches are far from being accurate enough to use in realworld applications. In this paper, we first perform in-depth error analysis of the current best performing definition detection system and discover major causes of errors. Based on this analysis, we develop a new definition detection system, HEDDEx, that utilizes syntactic features, transformer encoders, and heuristic filters, and evaluate it on a standard sentence-level benchmark. Because current benchmarks evaluate randomly sampled sentences, we propose an alternative evaluation that assesses every sentence within a document. This allows for evaluating recall in addition to precision. HEDDEx outperforms the leading system on both the sentence-level and the document-level tasks, by 12.7 F1 points and 14.4 F1 points, respectively. We note that performance on the high-recall document-level task is much lower than in the standard evaluation approach, due to the necessity of incorporation of document structure as features. We discuss remaining challenges in document-level definition detection, ideas for improvements, and potential issues for the development of reading aid applications.",38940724,https://www.aclweb.org/anthology/2020.sdp-1.22 +WS-7,WS-7.43shared,"IIITBH-IITP@CL-SciSumm20, CL-LaySumm20, LongSumm20",Saichethan Miriyala Reddy|Naveen Saini|Sriparna Saha|Pushpak Bhattacharyya,"In this paper, we present the IIIT Bhagalpur and IIT Patna team’s effort to solve the three shared tasks namely, CL-SciSumm 2020, CL-LaySumm 2020, LongSumm 2020 at SDP 2020. The theme of these tasks is to generate medium-scale, lay and long summaries, respectively, for scientific articles. For the first two tasks, unsupervised systems are developed, while for the third one, we develop a supervised system.The performances of all the systems were evaluated on the associated datasets with the shared tasks in term of well-known ROUGE metric.",38940739,https://www.aclweb.org/anthology/2020.sdp-1.27 +WS-7,WS-7.44,Reconstructing Manual Information Extraction with DB-to-Document Backprojection: Experiments in the Life Science Domain,Mark-Christoph Muller|Sucheta Ghosh|Maja Rey|Ulrike Wittig|Wolfgang Muller|Michael Strube,"We introduce a novel scientific document processing task for making previously inaccessible information in printed paper documents available to automatic processing. We describe our data set of scanned documents and data records from the biological database SABIO-RK, provide a definition of the task, and report findings from preliminary experiments. Rigorous evaluation proved challenging due to lack of gold-standard data and a difficult notion of correctness. Qualitative inspection of results, however, showed the feasibility and usefulness of the task",38940718,https://www.aclweb.org/anthology/2020.sdp-1.9 +WS-7,WS-7.45,"AUTH @ CLSciSumm 20, LaySumm 20, LongSumm 20",Alexios Gidiotis|Stefanos Stefanidis|Grigorios Tsoumakas,"We present the systems we submitted for the shared tasks of the Workshop on Scholarly Document Processing at EMNLP 2020. Our approaches to the tasks are focused on exploiting large Transformer models pre-trained on huge corpora and adapting them to the different shared tasks. For tasks 1A and 1B of CL-SciSumm we are using different variants of the BERT model to tackle the tasks of “cited text span” and “facet” identification. For the summarization tasks 2 of CL-SciSumm, LaySumm and LongSumm we make use of different variants of the PEGASUS model, with and without fine-tuning, adapted to the nuances of each one of those particular tasks.",38941222,https://www.aclweb.org/anthology/2020.sdp-1.28 +WS-7,WS-7.48_2shared,"SUMMAFORMERS @ LAYSUMM 20, LONGSUMM 20",Sayar Ghosh Roy|Nikhil Pinnaparaju|Risubh Jain|Manish Gupta|Vasudeva Varma,"Automatic text summarization has been widely studied as an important task in natural language processing. Traditionally, various feature engineering and machine learning based systems have been proposed for extractive as well as abstractive text summarization. Recently, deep learning based, specifically Transformer-based systems have been immensely popular. Summarization is a cognitively challenging task – extracting summary worthy sentences is laborious, and expressing semantics in brief when doing abstractive summarization is complicated. In this paper, we specifically look at the problem of summarizing scientific research papers from multiple domains. We differentiate between two types of summaries, namely, (a) LaySumm: A very short summary that captures the essence of the research paper in layman terms restricting overtly specific technical jargon and (b) LongSumm: A much longer detailed summary aimed at providing specific insights into various ideas touched upon in the paper. While leveraging latest Transformer-based models, our systems are simple, intuitive and based on how specific paper sections contribute to human summaries of the two types described above. Evaluations against gold standard summaries using ROUGE metrics prove the effectiveness of our approach. On blind test corpora, our system ranks first and third for the LongSumm and LaySumm tasks respectively.",38940742,https://www.aclweb.org/anthology/2020.sdp-1.39 WS-7,WS-7.48shared,Scientific Document Summarization for LaySumm '20 and LongSumm '2,Sayar Ghosh Roy|Nikhil Pinnaparaju|Risubh Jain|Manish Gupta∗|Vasudeva Varma,,38940738, -WS-7,WS-7.49,UniHD@CL-SciSumm 2020: Citation Extraction as Search,Dennis Aumiller|Satya Almasian|Philip Hausner|Michael Gertz,,38941224,https://www.aclweb.org/anthology/2020.sdp-1.29 -WS-7,WS-7.5,"On the effectiveness of small, discriminatively pre-trained language representation models for biomedical text mining",Ibrahim Burak Ozyurt,,38940735,https://www.aclweb.org/anthology/2020.sdp-1.12 -WS-7,WS-7.54,CMU: CiteQA@CLSciSumm 2020,Anjana Umapathy|Karthik Radhakrishnan|Kinjal Jain|Rahul Singh,,38941225,https://www.aclweb.org/anthology/2020.sdp-1.34 -WS-7,WS-7.56shared,GUIR @ LongSumm 2020: Learning to Generate Long Summaries from Scientific Documents,Sajad Sotudeh Gharebagh|Arman Cohan|Nazli Goharian,,38940737,https://www.aclweb.org/anthology/2020.sdp-1.41 -WS-7,WS-7.6,The impact of preprint servers in the formation of novel ideas,Swarup Satish|Zonghai Yao|Rew Drozdov|Boris Veytsman,,38940715,https://www.aclweb.org/anthology/2020.sdp-1.6 -WS-7,WS-7.60,Covidex: Neural Ranking Models and Keyword Search Infrastructure for the COVID-19 Open Research Dataset.,Edwin Zhang|Nikhil Gupta|Raphael Tang|Xiao Han|Ronak Pradeep|Kuang Lu|Yue Zhang|Rodrigo Nogueira|Kyunghyun Cho|Hui Fang|Jimmy Lin,,38940714,https://www.aclweb.org/anthology/2020.sdp-1.5 -WS-7,WS-7.61,A New Neural Search and Insights Platform for Navigating and Organizing AI Research,Marzieh Fadaee|Olga Gureenkova|Fernando Rejon Barrera|Carsten Schnober|Wouter Weerkamp|Jakub Zavrel,,38940726,https://www.aclweb.org/anthology/2020.sdp-1.23 -WS-7,WS-7.758,Where to Submit? Helping Researchers to Choose the Right Venue,Konstantin Kobs|Tobias Koopmann|Albin Zehe|David Fernes|Philipp Krop|Reas Hotho,,38940736,https://www.aclweb.org/anthology/2020.findings-emnlp.78 -WS-7,WS-7.8,Effective Distributed Representations for Academic Expert Search,Mark Berger|Jakub Zavrel|Paul Groth,,38940716,https://www.aclweb.org/anthology/2020.sdp-1.7 -WS-9,WS-9.10,"fugashi, a Tool for Tokenizing Japanese in Python",Paul McCann,,38939744,https://www.aclweb.org/anthology/2020.nlposs-1.7 -WS-9,WS-9.11,Going Beyond T-SNE: Exposing whatlies in Text Embeddings,Vincent Warmerdam|Thomas Kober|Rachael Tatman,,38939745,https://www.aclweb.org/anthology/2020.nlposs-1.8 -WS-9,WS-9.12,"Howl: A Deployed, Open-Source Wake Word Detection System",Raphael Tang|Jaejun Lee|Afsaneh Razi|Julia Cambre|Ian Bicking|Jofish Kaye|Jimmy Lin,,38939746,https://www.aclweb.org/anthology/2020.nlposs-1.9 -WS-9,WS-9.13,iNLTK: Natural Language Toolkit for Indic Languages,Gaurav Arora,,38939747,https://www.aclweb.org/anthology/2020.nlposs-1.10 -WS-9,WS-9.14,iobes: A Library for Span-Level Processing,Brian Lester,,38939748,https://www.aclweb.org/anthology/2020.nlposs-1.16 +WS-7,WS-7.49,UniHD@CL-SciSumm 2020: Citation Extraction as Search,Dennis Aumiller|Satya Almasian|Philip Hausner|Michael Gertz,"This work presents the entry by the team from Heidelberg University in the CL-SciSumm 2020 shared task at the Scholarly Document Processing workshop at EMNLP 2020. As in its previous iterations, the task is to highlight relevant parts in a reference paper, depending on a citance text excerpt from a citing paper. We participated in tasks 1A (citation identification) and 1B (citation context classification). Contrary to most previous works, we frame Task 1A as a search relevance problem, and introduce a 2-step re-ranking approach, which consists of a preselection based on BM25 in addition to positional document features, and a top-k re-ranking with BERT. For Task 1B, we follow previous submissions in applying methods that deal well with low resources and imbalanced classes.",38941224,https://www.aclweb.org/anthology/2020.sdp-1.29 +WS-7,WS-7.5,"On the effectiveness of small, discriminatively pre-trained language representation models for biomedical text mining",Ibrahim Burak Ozyurt,"Neural language representation models such as BERT have recently shown state of the art performance in downstream NLP tasks and bio-medical domain adaptation of BERT (Bio-BERT) has shown same behavior on biomedical text mining tasks. However, due to their large model size and resulting increased computational need, practical application of models such as BERT is challenging making smaller models with comparable performance desirable for real word applications. Recently, a new language transformers based language representation model named ELECTRA is introduced, that makes efficient usage of training data in a generative-discriminative neural model setting that shows performance gains over BERT. These gains are especially impressive for smaller models. Here, we introduce two small ELECTRA based model named Bio-ELECTRA and Bio-ELECTRA++ that are eight times smaller than BERT Base and Bio-BERT and achieves comparable or better performance on biomedical question answering, yes/no question answer classification, question answer candidate ranking and relation extraction tasks. Bio-ELECTRA is pre-trained from scratch on PubMed abstracts using a consumer grade GPU with only 8GB memory. Bio-ELECTRA++ is the further pre-trained version of Bio-ELECTRA trained on a corpus of open access full papers from PubMed Central. While, for biomedical named entity recognition, large BERT Base model outperforms Bio-ELECTRA++, Bio-ELECTRA and ELECTRA-Small++, with hyperparameter tuning Bio-ELECTRA++ achieves results comparable to BERT.",38940735,https://www.aclweb.org/anthology/2020.sdp-1.12 +WS-7,WS-7.54,CMU: CiteQA@CLSciSumm 2020,Anjana Umapathy|Karthik Radhakrishnan|Kinjal Jain|Rahul Singh,"In academic publications, citations are used to build context for a concept by highlighting relevant aspects from reference papers. Automatically identifying referenced snippets can help researchers swiftly isolate principal contributions of scientific works. In this paper, we exploit the underlying structure of scientific articles to predict reference paper spans and facets corresponding to a citation. We propose two methods to detect citation spans - keyphrase overlap, BERT along with structural priors. We fine-tune FastText embeddings and leverage textual, positional features to predict citation facets.",38941225,https://www.aclweb.org/anthology/2020.sdp-1.34 +WS-7,WS-7.56shared,GUIR @ LongSumm 2020: Learning to Generate Long Summaries from Scientific Documents,Sajad Sotudeh Gharebagh|Arman Cohan|Nazli Goharian,"This paper presents our methods for the LongSumm 2020: Shared Task on Generating Long Summaries for Scientific Documents, where the task is to generatelong summaries given a set of scientific papers provided by the organizers. We explore 3 main approaches for this task: 1. An extractive approach using a BERT-based summarization model; 2. A two stage model that additionally includes an abstraction step using BART; and 3. A new multi-tasking approach on incorporating document structure into the summarizer. We found that our new multi-tasking approach outperforms the two other methods by large margins. Among 9 participants in the shared task, our best model ranks top according to Rouge-1 score (53.11%) while staying competitive in terms of Rouge-2.",38940737,https://www.aclweb.org/anthology/2020.sdp-1.41 +WS-7,WS-7.6,The impact of preprint servers in the formation of novel ideas,Swarup Satish|Zonghai Yao|Rew Drozdov|Boris Veytsman,"We study whether novel ideas in biomedical literature appear first in preprints or traditional journals. We develop a Bayesian method to estimate the time of appearance for a phrase in the literature, and apply it to a number of phrases, both automatically extracted and suggested by experts. We see that presently most phrases appear first in the traditional journals, but there is a number of phrases with the first appearance on preprint servers. A comparison of the general composition of texts from bioRxiv and traditional journals shows a growing trend of bioRxiv being predictive of traditional journals. We discuss the application of the method for related problems.",38940715,https://www.aclweb.org/anthology/2020.sdp-1.6 +WS-7,WS-7.60,Covidex: Neural Ranking Models and Keyword Search Infrastructure for the COVID-19 Open Research Dataset.,Edwin Zhang|Nikhil Gupta|Raphael Tang|Xiao Han|Ronak Pradeep|Kuang Lu|Yue Zhang|Rodrigo Nogueira|Kyunghyun Cho|Hui Fang|Jimmy Lin,"We present Covidex, a search engine that exploits the latest neural ranking models to provide information access to the COVID-19 Open Research Dataset curated by the Allen Institute for AI. Our system has been online and serving users since late March 2020. The Covidex is the user application component of our three-pronged strategy to develop technologies for helping domain experts tackle the ongoing global pandemic. In addition, we provide robust and easy-to-use keyword search infrastructure that exploits mature fusion-based methods as well as standalone neural ranking models that can be incorporated into other applications. These techniques have been evaluated in the multi-round TREC-COVID challenge: Our infrastructure and baselines have been adopted by many participants, including some of the best systems. In round 3, we submitted the highest-scoring run that took advantage of previous training data and the second-highest fully automatic run. In rounds 4 and 5, we submitted the highest-scoring fully automatic runs.",38940714,https://www.aclweb.org/anthology/2020.sdp-1.5 +WS-7,WS-7.61,A New Neural Search and Insights Platform for Navigating and Organizing AI Research,Marzieh Fadaee|Olga Gureenkova|Fernando Rejon Barrera|Carsten Schnober|Wouter Weerkamp|Jakub Zavrel,"To provide AI researchers with modern tools for dealing with the explosive growth of the research literature in their field, we introduce a new platform, AI Research Navigator, that combines classical keyword search with neural retrieval to discover and organize relevant literature. The system provides search at multiple levels of textual granularity, from sentences to aggregations across documents, both in natural language and through navigation in a domain specific Knowledge Graph. We give an overview of the overall architecture of the system and of the components for document analysis, question answering, search, analytics, expert search, and recommendations.",38940726,https://www.aclweb.org/anthology/2020.sdp-1.23 +WS-7,WS-7.758,Where to Submit? Helping Researchers to Choose the Right Venue,Konstantin Kobs|Tobias Koopmann|Albin Zehe|David Fernes|Philipp Krop|Reas Hotho,"Whenever researchers write a paper, the same question occurs: “Where to submit?” In this work, we introduce WTS, an open and interpretable NLP system that recommends conferences and journals to researchers based on the title, abstract, and/or keywords of a given paper. We adapt the TextCNN architecture and automatically analyze its predictions using the Integrated Gradients method to highlight words and phrases that led to the recommendation of a scientific venue. We train and test our method on publications from the fields of artificial intelligence (AI) and medicine, both derived from the Semantic Scholar dataset. WTS achieves an Accuracy@5 of approximately 83% for AI papers and 95% in the field of medicine. It is open source and available for testing on https://wheretosubmit.ml.",38940736,https://www.aclweb.org/anthology/2020.findings-emnlp.78 +WS-7,WS-7.8,Effective Distributed Representations for Academic Expert Search,Mark Berger|Jakub Zavrel|Paul Groth,"Expert search aims to find and rank experts based on a user’s query. In academia, retrieving experts is an efficient way to navigate through a large amount of academic knowledge. Here, we study how different distributed representations of academic papers (i.e. embeddings) impact academic expert retrieval. We use the Microsoft Academic Graph dataset and experiment with different configurations of a document-centric voting model for retrieval. In particular, we explore the impact of the use of contextualized embeddings on search performance. We also present results for paper embeddings that incorporate citation information through retrofitting. Additionally, experiments are conducted using different techniques for assigning author weights based on author order. We observe that using contextual embeddings produced by a transformer model trained for sentence similarity tasks produces the most effective paper representations for document-centric expert retrieval. However, retrofitting the paper embeddings and using elaborate author contribution weighting strategies did not improve retrieval performance.",38940716,https://www.aclweb.org/anthology/2020.sdp-1.7 +WS-9,WS-9.10,"fugashi, a Tool for Tokenizing Japanese in Python",Paul McCann,"Recent years have seen an increase in the number of large-scale multilingual NLP projects. However, even in such projects, languages with special processing requirements are often excluded. One such language is Japanese. Japanese is written without spaces, tokenization is non-trivial, and while high quality open source tokenizers exist they can be hard to use and lack English documentation. This paper introduces fugashi, a MeCab wrapper for Python, and gives an introduction to tokenizing Japanese.",38939744,https://www.aclweb.org/anthology/2020.nlposs-1.7 +WS-9,WS-9.11,Going Beyond T-SNE: Exposing whatlies in Text Embeddings,Vincent Warmerdam|Thomas Kober|Rachael Tatman,"We introduce whatlies, an open source toolkit for visually inspecting word and sentence embeddings. The project offers a unified and extensible API with current support for a range of popular embedding backends including spaCy, tfhub, huggingface transformers, gensim, fastText and BytePair embeddings. The package combines a domain specific language for vector arithmetic with visualisation tools that make exploring word embeddings more intuitive and concise. It offers support for many popular dimensionality reduction techniques as well as many interactive visualisations that can either be statically exported or shared via Jupyter notebooks. The project documentation is available from https://rasahq.github.io/whatlies/.",38939745,https://www.aclweb.org/anthology/2020.nlposs-1.8 +WS-9,WS-9.12,"Howl: A Deployed, Open-Source Wake Word Detection System",Raphael Tang|Jaejun Lee|Afsaneh Razi|Julia Cambre|Ian Bicking|Jofish Kaye|Jimmy Lin,"We describe Howl, an open-source wake word detection toolkit with native support for open speech datasets such as Mozilla Common Voice (MCV) and Google Speech Commands (GSC). We report benchmark results of various models supported by our toolkit on GSC and our own freely available wake word detection dataset, built from MCV. One of our models is deployed in Firefox Voice, a plugin enabling speech interactivity for the Firefox web browser. Howl represents, to the best of our knowledge, the first fully productionized, open-source wake word detection toolkit with a web browser deployment target. Our codebase is at howl.ai.",38939746,https://www.aclweb.org/anthology/2020.nlposs-1.9 +WS-9,WS-9.13,iNLTK: Natural Language Toolkit for Indic Languages,Gaurav Arora,"We present iNLTK, an open-source NLP library consisting of pre-trained language models and out-of-the-box support for Data Augmentation, Textual Similarity, Sentence Embeddings, Word Embeddings, Tokenization and Text Generation in 13 Indic Languages. By using pre-trained models from iNLTK for text classification on publicly available datasets, we significantly outperform previously reported results. On these datasets, we also show that by using pre-trained models and data augmentation from iNLTK, we can achieve more than 95% of the previous best performance by using less than 10% of the training data. iNLTK is already being widely used by the community and has 40,000+ downloads, 600+ stars and 100+ forks on GitHub. The library is available at https://github.com/goru001/inltk.",38939747,https://www.aclweb.org/anthology/2020.nlposs-1.10 +WS-9,WS-9.14,iobes: A Library for Span-Level Processing,Brian Lester,"Many tasks in natural language processing, such as named entity recognition and slot-filling, involve identifying and labeling specific spans of text. In order to leverage common models, these tasks are often recast as sequence labeling tasks. Each token is given a label and these labels are prefixed with special tokens such as B- or I-. After a model assigns labels to each token, these prefixes are used to group the tokens into spans. Properly parsing these annotations is critical for producing fair and comparable metrics; however, despite its importance, there is not an easy-to-use, standardized, programmatically integratable library to help work with span labeling. To remedy this, we introduce our open-source library, iobes. iobes is used for parsing, converting, and processing spans represented as token level decisions.",38939748,https://www.aclweb.org/anthology/2020.nlposs-1.16 WS-9,WS-9.15,jiant: A Software Toolkit for Research on General-Purpose Text Understanding Models,Yada Pruksachatkun|Phil Yeres|Haokun Liu|Jason Phang|Phu Mon Htut|Alex Wang|Ian Tenney|Samuel R. Bowman,,38939749, -WS-9,WS-9.16,KLPT – Kurdish Language Processing Toolkit,Sina Ahmadi,,38939750,https://www.aclweb.org/anthology/2020.nlposs-1.11 -WS-9,WS-9.17,Open Korean Corpora: A Practical Report,Won Ik Cho|Sangwhan Moon|Youngsook Song,,38939751,https://www.aclweb.org/anthology/2020.nlposs-1.12 -WS-9,WS-9.18,Open-Source Morphology for Endangered Mordvinic Languages,Jack Rueter|Mika Hämäläinen|Niko Partanen,,38939752,https://www.aclweb.org/anthology/2020.nlposs-1.13 -WS-9,WS-9.19,Pimlico: A toolkit for corpus-processing pipelines and reproducible experiments,Mark Granroth-Wilding,,38939753,https://www.aclweb.org/anthology/2020.nlposs-1.14 -WS-9,WS-9.20,PySBD: Pragmatic Sentence Boundary Disambiguation,Nipun Sadvilkar|Mark Neumann,,38939754,https://www.aclweb.org/anthology/2020.nlposs-1.15 -WS-9,WS-9.21,SacreROUGE: An Open-Source Library for Using and Developing Summarization Evaluation Metrics,Daniel Deutsch|Dan Roth,,38939755,https://www.aclweb.org/anthology/2020.nlposs-1.17 -WS-9,WS-9.22,TextAttack: Lessons learned in designing Python frameworks for NLP,John Morris|Jin Yong Yoo|Yanjun Qi,,38939756,https://www.aclweb.org/anthology/2020.nlposs-1.18 -WS-9,WS-9.23,"TOMODAPI: A Topic Modeling API to Train, Use and Compare Topic Models",Pasquale Lisena|Ismail Harrando|Oussama Kandakji|Raphael Troncy,,38939757,https://www.aclweb.org/anthology/2020.nlposs-1.19 -WS-9,WS-9.24,User-centered Robust NLP OSS: Lessons Learned from Developing Maintaining RSMTool,Nitin Madnani|Anastassia Loukina,,38939758,https://www.aclweb.org/anthology/2020.nlposs-1.20 -WS-9,WS-9.25,WAFFLE: A Graph for WordNet Applied to FreeForm Linguistic Exploration,Berk Ekmekci|Blake Howald,,38939759,https://www.aclweb.org/anthology/2020.nlposs-1.21 -WS-9,WS-9.4,A Framework to Assist Chat Operators of Mental Healthcare Services,Thiago Madeira|Heder Bernardino|Jairo Francisco De Souza|Henrique Gomide|Nathália Munck Machado|Bruno Marcos Pinheiro Da Silva|Alexandre Vieira Pereira Pacelli,,38939738,https://www.aclweb.org/anthology/2020.nlposs-1.1 -WS-9,WS-9.5,ARBML: Democritizing Arabic Natural Language Processing Tools,Zaid Alyafeai|Maged Al-Shaibani,,38939739,https://www.aclweb.org/anthology/2020.nlposs-1.2 -WS-9,WS-9.6,CLEVR Parser: A Graph Parser Library for Geometric Learning on Language Grounded Image Scenes,Raeid Saqur|Ameet Deshpande,,38939740,https://www.aclweb.org/anthology/2020.nlposs-1.3 -WS-9,WS-9.7,End-to-end NLP Pipelines in Rust,Guillaume Becquin,,38939741,https://www.aclweb.org/anthology/2020.nlposs-1.4 -WS-9,WS-9.8,Fair Embedding Engine: A Library for Analyzing and Mitigating Gender Bias in Word Embeddings,Vaibhav Kumar|Tenzin Bhotia|Vaibhav Kumar,,38939742,https://www.aclweb.org/anthology/2020.nlposs-1.5 -WS-9,WS-9.9,Flexible retrieval with NMSLIB and FlexNeuART,Leonid Boytsov|Eric Nyberg,,38939743,https://www.aclweb.org/anthology/2020.nlposs-1.6 +WS-9,WS-9.16,KLPT – Kurdish Language Processing Toolkit,Sina Ahmadi,"Despite the recent advances in applying language-independent approaches to various natural language processing tasks thanks to artificial intelligence, some language-specific tools are still essential to process a language in a viable manner. Kurdish language is a less-resourced language with a remarkable diversity in dialects and scripts and lacks basic language processing tools. To address this issue, we introduce a language processing toolkit to handle such a diversity in an efficient way. Our toolkit is composed of fundamental components such as text preprocessing, stemming, tokenization, lemmatization and transliteration and is able to get further extended by future developers. The project is publicly available.",38939750,https://www.aclweb.org/anthology/2020.nlposs-1.11 +WS-9,WS-9.17,Open Korean Corpora: A Practical Report,Won Ik Cho|Sangwhan Moon|Youngsook Song,"Korean is often referred to as a low-resource language in the research community. While this claim is partially true, it is also because the availability of resources is inadequately advertised and curated. This work curates and reviews a list of Korean corpora, first describing institution-level resource development, then further iterate through a list of current open datasets for different types of tasks. We then propose a direction on how open-source dataset construction and releases should be done for less-resourced languages to promote research.",38939751,https://www.aclweb.org/anthology/2020.nlposs-1.12 +WS-9,WS-9.18,Open-Source Morphology for Endangered Mordvinic Languages,Jack Rueter|Mika Hämäläinen|Niko Partanen,"This document describes shared development of finite-state description of two closely related but endangered minority languages, Erzya and Moksha. It touches upon morpholexical unity and diversity of the two languages and how this provides a motivation for shared open-source FST development. We describe how we have designed the transducers so that they can benefit from existing open-source infrastructures and are as reusable as possible.",38939752,https://www.aclweb.org/anthology/2020.nlposs-1.13 +WS-9,WS-9.19,Pimlico: A toolkit for corpus-processing pipelines and reproducible experiments,Mark Granroth-Wilding,"We present Pimlico, an open source toolkit for building pipelines for processing large corpora. It is especially focused on processing linguistic corpora and provides wrappers around existing, widely used NLP tools. A particular goal is to ease distribution of reproducible and extensible experiments by making it easy to document and re-run all steps involved, including data loading, pre-processing, model training and evaluation. Once a pipeline is released, it is easy to adapt, for example, to run on a new dataset, or to re-run an experiment with different parameters. The toolkit takes care of many common challenges in writing and distributing corpus-processing code, such as managing data between the steps of a pipeline, installing required software and combining existing toolkits with new, task-specific code.",38939753,https://www.aclweb.org/anthology/2020.nlposs-1.14 +WS-9,WS-9.20,PySBD: Pragmatic Sentence Boundary Disambiguation,Nipun Sadvilkar|Mark Neumann,"We present a rule-based sentence boundary disambiguation Python package that works out-of-the-box for 22 languages. We aim to provide a realistic segmenter which can provide logical sentences even when the format and domain of the input text is unknown. In our work, we adapt the Golden Rules Set (a language specific set of sentence boundary exemplars) originally implemented as a ruby gem pragmatic segmenter which we ported to Python with additional improvements and functionality. PySBD passes 97.92% of the Golden Rule Set examplars for English, an improvement of 25% over the next best open source Python tool.",38939754,https://www.aclweb.org/anthology/2020.nlposs-1.15 +WS-9,WS-9.21,SacreROUGE: An Open-Source Library for Using and Developing Summarization Evaluation Metrics,Daniel Deutsch|Dan Roth,"We present SacreROUGE, an open-source library for using and developing summarization evaluation metrics. SacreROUGE removes many obstacles that researchers face when using or developing metrics: (1) The library provides Python wrappers around the official implementations of existing evaluation metrics so they share a common, easy-to-use interface; (2) it provides functionality to evaluate how well any metric implemented in the library correlates to human-annotated judgments, so no additional code needs to be written for a new evaluation metric; and (3) it includes scripts for loading datasets that contain human judgments so they can easily be used for evaluation. This work describes the design of the library, including the core Metric interface, the command-line API for evaluating summarization models and metrics, and the scripts to load and reformat publicly available datasets. The development of SacreROUGE is ongoing and open to contributions from the community.",38939755,https://www.aclweb.org/anthology/2020.nlposs-1.17 +WS-9,WS-9.22,TextAttack: Lessons learned in designing Python frameworks for NLP,John Morris|Jin Yong Yoo|Yanjun Qi,"TextAttack is an open-source Python toolkit for adversarial attacks, adversarial training, and data augmentation in NLP. TextAttack unites 15+ papers from the NLP adversarial attack literature into a single framework, with many components reused across attacks. This framework allows both researchers and developers to test and study the weaknesses of their NLP models. To build such an open-source NLP toolkit requires solving some common problems: How do we enable users to supply models from different deep learning frameworks? How can we build tools to support as many different datasets as possible? We share our insights into developing a well-written, well-documented NLP Python framework in hope that they can aid future development of similar packages.",38939756,https://www.aclweb.org/anthology/2020.nlposs-1.18 +WS-9,WS-9.23,"TOMODAPI: A Topic Modeling API to Train, Use and Compare Topic Models",Pasquale Lisena|Ismail Harrando|Oussama Kandakji|Raphael Troncy,"From LDA to neural models, different topic modeling approaches have been proposed in the literature. However, their suitability and performance is not easy to compare, particularly when the algorithms are being used in the wild on heterogeneous datasets. In this paper, we introduce ToModAPI (TOpic MOdeling API), a wrapper library to easily train, evaluate and infer using different topic modeling algorithms through a unified interface. The library is extensible and can be used in Python environments or through a Web API.",38939757,https://www.aclweb.org/anthology/2020.nlposs-1.19 +WS-9,WS-9.24,User-centered Robust NLP OSS: Lessons Learned from Developing Maintaining RSMTool,Nitin Madnani|Anastassia Loukina,"For the last 5 years, we have developed and maintained RSMTool – an open-source tool for evaluating NLP systems that automatically score written and spoken responses. RSMTool is designed to be cross-disciplinary, borrowing heavily from NLP, machine learning, and educational measurement. Its cross-disciplinary nature has required us to learn a user-centered development approach in terms of both design and implementation. We share some of these lessons in this paper.",38939758,https://www.aclweb.org/anthology/2020.nlposs-1.20 +WS-9,WS-9.25,WAFFLE: A Graph for WordNet Applied to FreeForm Linguistic Exploration,Berk Ekmekci|Blake Howald,"The WordNet database of English (Fellbaum, 1998) is a key source of semantic information for research and development of natural language processing applications. As the sophistication of these applications increases with the use of large datasets, deep learning, and graph-based methods, so should the use of WordNet. To this end, we introduce WAFFLE: WordNet Applied to FreeForm Linguistic Exploration which makes WordNet available in an open source graph data structure. The WAFFLE graph relies on platform agnostic formats for robust interrogation and flexibility. Where existing implementations of WordNet offer dictionary-like lookup, single degree neighborhood operations, and path based similarity-scoring, the WAFFLE graph makes all nodes (semantic relation sets) and relationships queryable at scale, enabling local and global analysis of all relationships without the need for custom code. We demonstrate WAFFLE’s ease of use, visualization capabilities, and scalable efficiency with common queries, operations, and interactions. WAFFLE is available at github.com/TRSS-NLP/WAFFLE.",38939759,https://www.aclweb.org/anthology/2020.nlposs-1.21 +WS-9,WS-9.4,A Framework to Assist Chat Operators of Mental Healthcare Services,Thiago Madeira|Heder Bernardino|Jairo Francisco De Souza|Henrique Gomide|Nathália Munck Machado|Bruno Marcos Pinheiro Da Silva|Alexandre Vieira Pereira Pacelli,"Conversational agents can be used to make diagnoses, classify mental states, promote health education, and provide emotional support. The benefits of adopting conversational agents include widespread access, increased treatment engagement, and improved patient relationships with the intervention. We propose here a framework to assist chat operators of mental healthcare services, instead of a fully automated conversational agent. This design eases to avoid the adverse effects of applying chatbots in mental healthcare. The proposed framework is capable of improving the quality and reducing the time of interactions via chat between a user and a chat operator. We also present a case study in the context of health promotion on reducing tobacco use. The proposed framework uses artificial intelligence, specifically natural language processing (NLP) techniques, to classify messages from chat users. A list of suggestions is offered to the chat operator, with topics to be discussed in the session. These suggestions were created based on service protocols and the classification of previous chat sessions. The operator can also edit the suggested messages. Data collected can be used in the future to improve the quality of the suggestions offered.",38939738,https://www.aclweb.org/anthology/2020.nlposs-1.1 +WS-9,WS-9.5,ARBML: Democritizing Arabic Natural Language Processing Tools,Zaid Alyafeai|Maged Al-Shaibani,"Automating natural language understanding is a lifelong quest addressed for decades. With the help of advances in machine learning and particularly, deep learning, we are able to produce state of the art models that can imitate human interactions with languages. Unfortunately, these advances are controlled by the availability of language resources. Arabic advances in this field , although it has a great potential, are still limited. This is apparent in both research and development. In this paper, we showcase some NLP models we trained for Arabic. We also present our methodology and pipeline to build such models from data collection, data preprocessing, tokenization and model deployment. These tools help in the advancement of the field and provide a systematic approach for extending NLP tools to many languages.",38939739,https://www.aclweb.org/anthology/2020.nlposs-1.2 +WS-9,WS-9.6,CLEVR Parser: A Graph Parser Library for Geometric Learning on Language Grounded Image Scenes,Raeid Saqur|Ameet Deshpande,"The CLEVR dataset has been used extensively in language grounded visual reasoning in Machine Learning (ML) and Natural Language Processing (NLP). We present a graph parser library for CLEVR, that provides functionalities for object-centric attributes and relationships extraction, and construction of structural graph representations for dual modalities. Structural order-invariant representations enable geometric learning and can aid in downstream tasks like language grounding to vision, robotics, compositionality, interpretability, and computational grammar construction. We provide three extensible main components – parser, embedder, and visualizer that can be tailored to suit specific learning setups. We also provide out-of-the-box functionality for seamless integration with popular deep graph neural network (GNN) libraries. Additionally, we discuss downstream usage and applications of the library, and how it can accelerate research for the NLP community.",38939740,https://www.aclweb.org/anthology/2020.nlposs-1.3 +WS-9,WS-9.7,End-to-end NLP Pipelines in Rust,Guillaume Becquin,"The recent progress in natural language processing research has been supported by the development of a rich open source ecosystem in Python. Libraries allowing NLP practitioners but also non-specialists to leverage state-of-the-art models have been instrumental in the democratization of this technology. The maturity of the open-source NLP ecosystem however varies between languages. This work proposes a new open-source library aimed at bringing state-of-the-art NLP to Rust. Rust is a systems programming language for which the foundations required to build machine learning applications are available but still lacks ready-to-use, end-to-end NLP libraries. The proposed library, rust-bert, implements modern language models and ready-to-use pipelines (for example translation or summarization). This allows further development by the Rust community from both NLP experts and non-specialists. It is hoped that this library will accelerate the development of the NLP ecosystem in Rust. The library is under active development and available at https://github.com/guillaume-be/rust-bert.",38939741,https://www.aclweb.org/anthology/2020.nlposs-1.4 +WS-9,WS-9.8,Fair Embedding Engine: A Library for Analyzing and Mitigating Gender Bias in Word Embeddings,Vaibhav Kumar|Tenzin Bhotia|Vaibhav Kumar,"Non-contextual word embedding models have been shown to inherit human-like stereotypical biases of gender, race and religion from the training corpora. To counter this issue, a large body of research has emerged which aims to mitigate these biases while keeping the syntactic and semantic utility of embeddings intact. This paper describes Fair Embedding Engine (FEE), a library for analysing and mitigating gender bias in word embeddings. FEE combines various state of the art techniques for quantifying, visualising and mitigating gender bias in word embeddings under a standard abstraction. FEE will aid practitioners in fast track analysis of existing debiasing methods on their embedding models. Further, it will allow rapid prototyping of new methods by evaluating their performance on a suite of standard metrics.",38939742,https://www.aclweb.org/anthology/2020.nlposs-1.5 +WS-9,WS-9.9,Flexible retrieval with NMSLIB and FlexNeuART,Leonid Boytsov|Eric Nyberg,"Our objective is to introduce to the NLP community NMSLIB, describe a new retrieval toolkit FlexNeuART, as well as their integration capabilities. NMSLIB, while being one the fastest k-NN search libraries, is quite generic and supports a variety of distance/similarity functions. Because the library relies on the distance-based structure-agnostic algorithms, it can be further extended by adding new distances. FlexNeuART is a modular, extendible and flexible toolkit for candidate generation in IR and QA applications, which supports mixing of classic and neural ranking signals. FlexNeuART can efficiently retrieve mixed dense and sparse representations (with weights learned from training data), which is achieved by extending NMSLIB. In that, other retrieval systems work with purely sparse representations (e.g., Lucene), purely dense representations (e.g., FAISS and Annoy), or only perform mixing at the re-ranking stage.",38939743,https://www.aclweb.org/anthology/2020.nlposs-1.6 diff --git a/templates/paper.html b/templates/paper.html index 9c9b045..7f42d7b 100644 --- a/templates/paper.html +++ b/templates/paper.html @@ -74,9 +74,12 @@

+ {% if paper.content.abstract %} Abstract + {% endif %} + {% if paper.content.pdf_url %} Paper From e43f599f64769269eb890d3a7e9d2ab13307e155 Mon Sep 17 00:00:00 2001 From: Jan-Christoph Klie Date: Sun, 15 Nov 2020 02:30:26 +0100 Subject: [PATCH 4/5] #111 - Remove paper --- sitedata/main_papers.csv | 1 - 1 file changed, 1 deletion(-) diff --git a/sitedata/main_papers.csv b/sitedata/main_papers.csv index 9f73f4a..91b1349 100644 --- a/sitedata/main_papers.csv +++ b/sitedata/main_papers.csv @@ -261,7 +261,6 @@ main.2163,Direct Segmentation Models for Streaming Speech Translation,Javier Ira main.2164,Filtering Noisy Dialogue Corpora by Connectivity and Content Relatedness,Reina Akama|Sho Yokoi|Jun Suzuki|Kentaro Inui,"Large-scale dialogue datasets have recently become available for training neural dialogue agents. However, these datasets have been reported to contain a non-negligible number of unacceptable utterance pairs. In this paper, we propose a method for scoring the quality of utterance pairs in terms of their connectivity and relatedness. The proposed scoring method is designed based on findings widely shared in the dialogue and linguistics research communities. We demonstrate that it has a relatively good correlation with the human judgment of dialogue quality. Furthermore, the method is applied to filter out potentially unacceptable utterance pairs from a large-scale noisy dialogue corpus to ensure its quality. We experimentally confirm that training data filtered by the proposed method improves the quality of neural dialogue agents in response generation.",response generation|neural agents|scoring method|quality pairs,Dialog and Interactive Systems,Long,https://www.aclweb.org/anthology/2020.emnlp-main.68,38939062 main.2167,An Empirical Study on Large-Scale Multi-Label Text Classification Including Few and Zero-Shot Labels,Ilias Chalkidis|Manos Fergadiotis|Sotiris Kotitsas|Prodromos Malakasiotis|Nikolaos Aletras|Ion Androutsopoulos,"Large-scale Multi-label Text Classification (LMTC) has a wide range of Natural Language Processing (NLP) applications and presents interesting challenges. First, not all labels are well represented in the training set, due to the very large label set and the skewed label distributions of \lmtc datasets. Also, label hierarchies and differences in human labelling guidelines may affect graph-aware annotation proximity. Finally, the label hierarchies are periodically updated, requiring LMTC models capable of zero-shot generalization. Current state-of-the-art LMTC models employ Label-Wise Attention Networks (LWANs), which (1) typically treat LMTC as flat multi-label classification; (2) may use the label hierarchy to improve zero-shot learning, although this practice is vastly understudied; and (3) have not been combined with pre-trained Transformers (e.g. BERT), which have led to state-of-the-art results in several NLP benchmarks. Here, for the first time, we empirically evaluate a battery of LMTC methods from vanilla LWANs to hierarchical classification approaches and transfer learning, on frequent, few, and zero-shot learning on three datasets from different domains. We show that hierarchical methods based on Probabilistic Label Trees (PLTs) outperform LWANs. Furthermore, we show that Transformer-based approaches outperform the state-of-the-art in two of the datasets, and we propose a new state-of-the-art method which combines BERT with LWAN. Finally, we propose new models that leverage the label hierarchy to improve few and zero-shot learning, considering on each dataset a graph-aware annotation proximity measure that we introduce.",flat classification|hierarchical approaches|zero-shot learning|few learning,NLP Applications,Long,https://www.aclweb.org/anthology/2020.emnlp-main.607,38939063 main.2179,COGS: A Compositional Generalization Challenge Based on Semantic Interpretation,Najoung Kim|Tal Linzen,"Natural language is characterized by compositionality: the meaning of a complex expression is constructed from the meanings of its constituent parts. To facilitate the evaluation of the compositional abilities of language processing architectures, we introduce COGS, a semantic parsing dataset based on a fragment of English. The evaluation portion of COGS contains multiple systematic gaps that can only be addressed by compositional generalization; these include new combinations of familiar syntactic structures, or new combinations of familiar words and familiar structures. In experiments with Transformers and LSTMs, we found that in-distribution accuracy on the COGS test set was near-perfect (96--99%), but generalization accuracy was substantially lower (16--35%) and showed high sensitivity to random seed (+-6--8%). These findings indicate that contemporary standard NLP models are limited in their compositional generalization capacity, and position COGS as a good way to measure progress.",compositional generalization|language architectures|cogs|lstms,Interpretability and Analysis of Models for NLP,Long,https://www.aclweb.org/anthology/2020.emnlp-main.731,38939064 -main.2181,H2KGAT: Hierarchical Hyperbolic Knowledge Graph Attention Network,Shen Wang|Xiaokai Wei|Cicero Nogueira dos Santos|Zhiguo Wang|Ramesh Nallapati|Andrew Arnold|Bing Xiang|Philip S. Yu,"Knowledge Graphs encode rich relationships among large number of entities. Embedding entities and relations in low-dimensional space has shed light on representing knowledge graphs and reasoning over them, e.g., predicting missing relations between pairs of entities. Existing knowledge graph embedding approaches concentrate on modeling symmetry/asymmetry, inversion, and composition typed relations but overlook the hierarchical nature of relations. Recent studies have observed that there exist rich semantic hierarchical relations in knowledge graphs such as WordNet, where synsets are linked together in a hierarchy. To fill this gap, in this paper, we propose Hierarchical Hyperbolic Knowledge Graph Attention Network (H2KGAT), a novel knowledge graph embedding framework, which is able to better model and infer hierarchical relation patterns. Specifically, H2KGAT defines each entity in a hyperbolic polar embedding space. In addition, we propose an attentional neural context aggregator to enhance embedding learning, which can adaptively integrate the relational context. Our empirical study offers insights into the efficacy of modeling the semantic hierarchies in knowledge graphs, and we achieve significant performance gains compared to existing state-of-the-art methods on benchmark datasets for link prediction task, particularly at low dimensionality.",embedding learning|link task|hierarchical network|hkgat,Machine Learning for NLP,Long,https://www.aclweb.org/anthology/2020.emnlp-main.401,38939065 main.2198,Consistency of a Recurrent Language Model With Respect to Incomplete Decoding,Sean Welleck|Ilia Kulikov|Jaedeok Kim|Richard Yuanzhe Pang|Kyunghyun Cho,"Despite strong performance on a variety of tasks, neural sequence models trained with maximum likelihood have been shown to exhibit issues such as length bias and degenerate repetition. We study the related issue of receiving infinite-length sequences from a recurrent language model when using common decoding algorithms. To analyze this issue, we first define inconsistency of a decoding algorithm, meaning that the algorithm can yield an infinite-length sequence that has zero probability under the model. We prove that commonly used incomplete decoding algorithms – greedy search, beam search, top-k sampling, and nucleus sampling – are inconsistent, despite the fact that recurrent language models are trained to produce sequences of finite length. Based on these insights, we propose two remedies which address inconsistency: consistent variants of top-k and nucleus sampling, and a self-terminating recurrent language model. Empirical results show that inconsistency occurs in practice, and that the proposed methods prevent inconsistency.",receiving sequences|neural models|recurrent model|common algorithms,Language Generation,Long,https://www.aclweb.org/anthology/2020.emnlp-main.448,38939066 main.2205,MEGA RST Discourse Treebanks with Structure and Nuclearity from Scalable Distant Sentiment Supervision,Patrick Huber|Giuseppe Carenini,"The lack of large and diverse discourse treebanks hinders the application of data-driven approaches, such as deep-learning, to RST-style discourse parsing. In this work, we present a novel scalable methodology to automatically generate discourse treebanks using distant supervision from sentiment annotated datasets, creating and publishing MEGA-DT, a new large-scale discourse-annotated corpus. Our approach generates discourse trees incorporating structure and nuclearity for documents of arbitrary length by relying on an efficient heuristic beam-search strategy, extended with a stochastic component. Experiments on multiple datasets indicate that a discourse parser trained on our MEGA-DT treebank delivers promising inter-domain performance gains when compared to parsers trained on human-annotated discourse corpora.",rst-style parsing|data-driven approaches|deep-learning|scalable methodology,Discourse and Pragmatics,Long,https://www.aclweb.org/anthology/2020.emnlp-main.603,38939067 main.2208,Learning to Pronounce Chinese Without a Pronunciation Dictionary,Christopher Chu|Scot Fang|Kevin Knight,"We demonstrate a program that learns to pronounce Chinese text in Mandarin, without a pronunciation dictionary. From non-parallel streams of Chinese characters and Chinese pinyin syllables, it establishes a many-to-many mapping between characters and pronunciations. Using unsupervised methods, the program effectively deciphers writing into speech. Its token-level character-to-syllable accuracy is 89%, which significantly exceeds the 22% accuracy of prior work.",unsupervised methods|pronunciations|token-level accuracy|accuracy,"Phonology, Morphology and Word Segmentation",Long,https://www.aclweb.org/anthology/2020.emnlp-main.458,38939068 From f0d823ba62bcc0258b7d3aad1517fce500dbf309 Mon Sep 17 00:00:00 2001 From: Jan-Christoph Klie Date: Sun, 15 Nov 2020 02:33:10 +0100 Subject: [PATCH 5/5] #111 - Remove paper --- sitedata/paper_recs.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sitedata/paper_recs.json b/sitedata/paper_recs.json index d860323..44b4f35 100644 --- a/sitedata/paper_recs.json +++ b/sitedata/paper_recs.json @@ -1 +1 @@ -{"main.1004": ["main.1022", "main.959", "main.2228", "main.2380", "main.2117"], "main.1006": ["main.1201", "main.689", "main.1654", "main.1846", "main.128"], "main.1009": ["main.2839", "main.2927", "main.1113", "main.355", "main.1085"], "main.1010": ["main.782", "main.666", 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