EMNLP 2021main9 citations
CSAGN: Conversational Structure Aware Graph Network for Conversational Semantic Role Labeling
Abstract
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.
BibTeX
@inproceedings{wu-etal-2021-csagn,
title = "{CSAGN}: Conversational Structure Aware Graph Network for Conversational Semantic Role Labeling",
author = "Wu, Han and
Xu, Kun and
Song, Linqi",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.177/",
doi = "10.18653/v1/2021.emnlp-main.177",
pages = "2312--2317"
}