CSAGN: Conversational Structure Aware Graph Network for Conversational Semantic Role Labeling
Han Wu, Kun Xu, Linqi Song · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · 2021
Conversational semantic role labeling (CSRL) is believed to be a crucial step towards dialogue understanding.However, it remains a major challenge for existing CSRL parser to handle conversational structural information.In this paper, we present a simple and effective architecture for CSRL which aims to address this problem.Our model is based on a conversational structure-aware graph network which explicitly encodes the speaker dependent information.We also propose a multi-task learning method to further improve the model.Experimental results on benchmark datasets show that our model with our proposed training objectives significantly outperforms previous baselines.