@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",
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",
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.",
}
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%0 Conference Proceedings
%T CSAGN: Conversational Structure Aware Graph Network for Conversational Semantic Role Labeling
%A Wu, Han
%A Xu, Kun
%A Song, Linqi
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F wu-etal-2021-csagn
%X 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.
%R 10.18653/v1/2021.emnlp-main.177
%U https://aclanthology.org/2021.emnlp-main.177
%U https://doi.org/10.18653/v1/2021.emnlp-main.177
%P 2312-2317
Markdown (Informal)
[CSAGN: Conversational Structure Aware Graph Network for Conversational Semantic Role Labeling](https://aclanthology.org/2021.emnlp-main.177) (Wu et al., EMNLP 2021)
ACL