Feature Request: Semantic Role Labeling - Implement Syntactic and Semantic Role Labeling Models

[HsiangNianian]

Semantic role labeling (SRL) involves identifying the arguments in a sentence and assigning them semantic roles (e.g., agent, patient). This task will focus on building a model to label different roles in a sentence.

• Modeling: Should we use transformer models like BERT for SRL tasks?
• Data Sources: What training data should we use for SRL (e.g., PropBank, FrameNet)?
• Challenges: How do we handle ambiguities in role assignments?

Expected Outcome:

• A semantic role labeling system that can identify and label roles in sentences for better text understanding.
• Clear guidelines for integrating SRL with other NLP tasks.

Labels: feature, NLP, semantic-role-labeling