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        <title>Conversational Emotion-Cause Pair Extraction with Guided Mixture of Experts</title>
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        <namePart type="given">DongJin</namePart>
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            <namePart type="given">Andreas</namePart>
            <namePart type="family">Vlachos</namePart>
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    <abstract>Emotion-Cause Pair Extraction (ECPE) task aims to pair all emotions and corresponding causes in documents.ECPE is an important task for developing human-like responses. However, previous ECPE research is conducted based on news articles, which has different characteristics compared to dialogues. To address this issue, we propose a Pair-Relationship Guided Mixture-of-Experts (PRG-MoE) model, which considers dialogue features (e.g., speaker information).PRG-MoE automatically learns relationship between utterances and advises a gating network to incorporate dialogue features in the evaluation, yielding substantial performance improvement. We employ a new ECPE dataset, which is an English dialogue dataset, with more emotion-cause pairs in documents than news articles. We also propose Cause Type Classification that classifies emotion-cause pairs according to the types of the cause of a detected emotion. For reproducing the results, we make available all our code and data.</abstract>
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    <identifier type="doi">10.18653/v1/2023.eacl-main.240</identifier>
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%0 Conference Proceedings
%T Conversational Emotion-Cause Pair Extraction with Guided Mixture of Experts
%A Jeong, DongJin
%A Bak, JinYeong
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F jeong-bak-2023-conversational
%X Emotion-Cause Pair Extraction (ECPE) task aims to pair all emotions and corresponding causes in documents.ECPE is an important task for developing human-like responses. However, previous ECPE research is conducted based on news articles, which has different characteristics compared to dialogues. To address this issue, we propose a Pair-Relationship Guided Mixture-of-Experts (PRG-MoE) model, which considers dialogue features (e.g., speaker information).PRG-MoE automatically learns relationship between utterances and advises a gating network to incorporate dialogue features in the evaluation, yielding substantial performance improvement. We employ a new ECPE dataset, which is an English dialogue dataset, with more emotion-cause pairs in documents than news articles. We also propose Cause Type Classification that classifies emotion-cause pairs according to the types of the cause of a detected emotion. For reproducing the results, we make available all our code and data.
%R 10.18653/v1/2023.eacl-main.240
%U https://aclanthology.org/2023.eacl-main.240
%U https://doi.org/10.18653/v1/2023.eacl-main.240
%P 3288-3298

Markdown (Informal)

[Conversational Emotion-Cause Pair Extraction with Guided Mixture of Experts](https://aclanthology.org/2023.eacl-main.240) (Jeong & Bak, EACL 2023)

• Conversational Emotion-Cause Pair Extraction with Guided Mixture of Experts (Jeong & Bak, EACL 2023)

ACL

• DongJin Jeong and JinYeong Bak. 2023. Conversational Emotion-Cause Pair Extraction with Guided Mixture of Experts. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 3288–3298, Dubrovnik, Croatia. Association for Computational Linguistics.