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Incorporating contextual evidence to improve implicit discourse relation recognition in Chinese

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Abstract

The discourse analysis task, which focuses on understanding the semantics of long text spans, has received increasing attention in recent years. As a critical component of discourse analysis, discourse relation recognition aims to identify the rhetorical relations between adjacent discourse units (e.g., clauses, sentences, and sentence groups), called arguments, in a document. Previous works focused on capturing the semantic interactions between arguments to recognize their discourse relations, ignoring important textual information in the surrounding contexts. However, in many cases, more than capturing semantic interactions from the texts of the two arguments are needed to identify their rhetorical relations, requiring mining more contextual clues. In this paper, we propose a method to convert the RST-style discourse trees in the training set into dependency-based trees and train a contextual evidence selector on these transformed structures. In this way, the selector can learn the ability to automatically pick critical textual information from the context (i.e., as evidence) for arguments to assist in discriminating their relations. Then we encode the arguments concatenated with corresponding evidence to obtain the enhanced argument representations. Finally, we combine original and enhanced argument representations to recognize their relations. In addition, we introduce auxiliary tasks to guide the training of the evidence selector to strengthen its selection ability. The experimental results on the Chinese CDTB dataset show that our method outperforms several state-of-the-art baselines in both micro and macro F1 scores.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61836007, 61773276) and the Priority Academic Program Development (PAPD) of Jiangsu Higher Education Institutions.

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Authors and Affiliations

  1. School of Computer Science and Technology, Soochow University, Suzhou, 215000, China

    Sheng Xu, Peifeng Li & Qiaoming Zhu

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  1. Sheng Xu
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  2. Peifeng Li
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  3. Qiaoming Zhu
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Corresponding author

Correspondence to Peifeng Li.

Additional information

Sheng Xu received the MS degree from Soochow University, China in 2019. He is now a PhD student in the School of Computer Science and Technology at Soochow University, China. His research interests include discourse relation recognition and event relation extraction.

Peifeng Li received his PhD degree in Computer Science from Soochow University, China in 2006. He has been a Professor in the School of Computer Science and Technology at Soochow University, China since 2015. His research interests include Chinese computing, information extraction, etc.

Qiaoming Zhu received his PhD degree in Computer Science from Soochow University, China in 2006. He is now a Professor in the School of Computer Science and Technology at Soochow University, China. His research interests include Chinese computing, discourse analysis, etc.

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Xu, S., Li, P. & Zhu, Q. Incorporating contextual evidence to improve implicit discourse relation recognition in Chinese. Front. Comput. Sci. 18, 183312 (2024). https://doi.org/10.1007/s11704-023-2503-4

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