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        <title>Effective Inter-Clause Modeling for End-to-End Emotion-Cause Pair Extraction</title>
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    <name type="personal">
        <namePart type="given">Penghui</namePart>
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        <namePart type="given">Jiahao</namePart>
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            <namePart type="given">Dan</namePart>
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    <abstract>Emotion-cause pair extraction aims to extract all emotion clauses coupled with their cause clauses from a given document. Previous work employs two-step approaches, in which the first step extracts emotion clauses and cause clauses separately, and the second step trains a classifier to filter out negative pairs. However, such pipeline-style system for emotion-cause pair extraction is suboptimal because it suffers from error propagation and the two steps may not adapt to each other well. In this paper, we tackle emotion-cause pair extraction from a ranking perspective, i.e., ranking clause pair candidates in a document, and propose a one-step neural approach which emphasizes inter-clause modeling to perform end-to-end extraction. It models the interrelations between the clauses in a document to learn clause representations with graph attention, and enhances clause pair representations with kernel-based relative position embedding for effective ranking. Experimental results show that our approach significantly outperforms the current two-step systems, especially in the condition of extracting multiple pairs in one document.</abstract>
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%0 Conference Proceedings
%T Effective Inter-Clause Modeling for End-to-End Emotion-Cause Pair Extraction
%A Wei, Penghui
%A Zhao, Jiahao
%A Mao, Wenji
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F wei-etal-2020-effective
%X Emotion-cause pair extraction aims to extract all emotion clauses coupled with their cause clauses from a given document. Previous work employs two-step approaches, in which the first step extracts emotion clauses and cause clauses separately, and the second step trains a classifier to filter out negative pairs. However, such pipeline-style system for emotion-cause pair extraction is suboptimal because it suffers from error propagation and the two steps may not adapt to each other well. In this paper, we tackle emotion-cause pair extraction from a ranking perspective, i.e., ranking clause pair candidates in a document, and propose a one-step neural approach which emphasizes inter-clause modeling to perform end-to-end extraction. It models the interrelations between the clauses in a document to learn clause representations with graph attention, and enhances clause pair representations with kernel-based relative position embedding for effective ranking. Experimental results show that our approach significantly outperforms the current two-step systems, especially in the condition of extracting multiple pairs in one document.
%R 10.18653/v1/2020.acl-main.289
%U https://aclanthology.org/2020.acl-main.289
%U https://doi.org/10.18653/v1/2020.acl-main.289
%P 3171-3181

Markdown (Informal)

[Effective Inter-Clause Modeling for End-to-End Emotion-Cause Pair Extraction](https://aclanthology.org/2020.acl-main.289) (Wei et al., ACL 2020)

• Effective Inter-Clause Modeling for End-to-End Emotion-Cause Pair Extraction (Wei et al., ACL 2020)

ACL

• Penghui Wei, Jiahao Zhao, and Wenji Mao. 2020. Effective Inter-Clause Modeling for End-to-End Emotion-Cause Pair Extraction. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3171–3181, Online. Association for Computational Linguistics.