This repository contains the code and resources for the project "PejorativITy: Disambiguating Pejorative Epithets to Improve Misogyny Detection in Italian Tweets" presented at LREC-COLING 2024.
Misogyny is often expressed through figurative language. Some neutral words can assume a negative connotation when functioning as pejorative epithets. Disambiguating the meaning of such terms might help the detection of misogyny. In order to address such task, we present PejorativITy, a novel corpus of 1,200 manually annotated Italian tweets for pejorative language at the word level and misogyny at the sentence level. We evaluate the impact of injecting information about disambiguated words into a model targeting misogyny detection. In particular, we explore two different approaches for injection: concatenation of pejorative information and substitution of ambiguous words with univocal terms. Our experimental results, both on our corpus and on two popular benchmarks on Italian tweets, show that both approaches lead to a major classification improvement, indicating that word sense disambiguation is a promising preliminary step for misogyny detection. Furthermore, we investigate LLMs’ understanding of pejorative epithets by means of contextual word embeddings analysis and prompting.
The datasets folder contains the pejorativITy corpus.
The list of anchors is in anchors.json.
All runnable scripts are in runnables folder.
Run training.py.
Eventually save weights in pretrained_weights/alberto_pej.
Run analyze_pretrained_embedding.py to analyze pre-trained AlBERTo contextual embeddings.
Run analyze_finetuned_embedding.py to analyze fine-tuned AlBERTO contextual embeddings.
Run anchor_frequency.py.
Arianna Muti: [email protected]
Federico Ruggeri: [email protected]
You can cite our work as follows:
@inproceedings{muti-etal-2024-pejorativity-disambiguating,
title = "{P}ejorativ{IT}y: Disambiguating Pejorative Epithets to Improve Misogyny Detection in {I}talian Tweets",
author = "Muti, Arianna and
Ruggeri, Federico and
Toraman, Cagri and
Barr{\'o}n-Cede{\~n}o, Alberto and
Algherini, Samuel and
Musetti, Lorenzo and
Ronchi, Silvia and
Saretto, Gianmarco and
Zapparoli, Caterina",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.1112",
pages = "12700--12711",
abstract = "Misogyny is often expressed through figurative language. Some neutral words can assume a negative connotation when functioning as pejorative epithets. Disambiguating the meaning of such terms might help the detection of misogyny. In order to address such task, we present PejorativITy, a novel corpus of 1,200 manually annotated Italian tweets for pejorative language at the word level and misogyny at the sentence level. We evaluate the impact of injecting information about disambiguated words into a model targeting misogyny detection. In particular, we explore two different approaches for injection: concatenation of pejorative information and substitution of ambiguous words with univocal terms. Our experimental results, both on our corpus and on two popular benchmarks on Italian tweets, show that both approaches lead to a major classification improvement, indicating that word sense disambiguation is a promising preliminary step for misogyny detection. Furthermore, we investigate LLMs{'} understanding of pejorative epithets by means of contextual word embeddings analysis and prompting.",
}
This project is licensed under CC.BY License.