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        <title>E-CORE: Emotion Correlation Enhanced Empathetic Dialogue Generation</title>
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        <namePart type="given">Fengyi</namePart>
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        <namePart type="given">Lei</namePart>
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        <namePart type="given">Quan</namePart>
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    <name type="personal">
        <namePart type="given">Zhendong</namePart>
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            <title>Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing</title>
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    <abstract>Achieving empathy is a crucial step toward humanized dialogue systems. Current approaches for empathetic dialogue generation mainly perceive an emotional label to generate an empathetic response conditioned on it, which simply treat emotions independently, but ignore the intrinsic emotion correlation in dialogues, resulting in inaccurate emotion perception and unsuitable response generation. In this paper, we propose a novel emotion correlation enhanced empathetic dialogue generation framework, which comprehensively realizes emotion correlation learning, utilization, and supervising. Specifically, a multi-resolution emotion graph is devised to capture context-based emotion interactions from different resolutions, further modeling emotion correlation. Then we propose an emotion correlation enhanced decoder, with a novel correlation-aware aggregation and soft/hard strategy, respectively improving the emotion perception and response generation. Experimental results on the benchmark dataset demonstrate the superiority of our model in both empathetic perception and expression.</abstract>
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    <identifier type="doi">10.18653/v1/2023.emnlp-main.653</identifier>
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%0 Conference Proceedings
%T E-CORE: Emotion Correlation Enhanced Empathetic Dialogue Generation
%A Fu, Fengyi
%A Zhang, Lei
%A Wang, Quan
%A Mao, Zhendong
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F fu-etal-2023-e
%X Achieving empathy is a crucial step toward humanized dialogue systems. Current approaches for empathetic dialogue generation mainly perceive an emotional label to generate an empathetic response conditioned on it, which simply treat emotions independently, but ignore the intrinsic emotion correlation in dialogues, resulting in inaccurate emotion perception and unsuitable response generation. In this paper, we propose a novel emotion correlation enhanced empathetic dialogue generation framework, which comprehensively realizes emotion correlation learning, utilization, and supervising. Specifically, a multi-resolution emotion graph is devised to capture context-based emotion interactions from different resolutions, further modeling emotion correlation. Then we propose an emotion correlation enhanced decoder, with a novel correlation-aware aggregation and soft/hard strategy, respectively improving the emotion perception and response generation. Experimental results on the benchmark dataset demonstrate the superiority of our model in both empathetic perception and expression.
%R 10.18653/v1/2023.emnlp-main.653
%U https://aclanthology.org/2023.emnlp-main.653/
%U https://doi.org/10.18653/v1/2023.emnlp-main.653
%P 10568-10586

Markdown (Informal)

[E-CORE: Emotion Correlation Enhanced Empathetic Dialogue Generation](https://aclanthology.org/2023.emnlp-main.653/) (Fu et al., EMNLP 2023)

• E-CORE: Emotion Correlation Enhanced Empathetic Dialogue Generation (Fu et al., EMNLP 2023)

ACL

• Fengyi Fu, Lei Zhang, Quan Wang, and Zhendong Mao. 2023. E-CORE: Emotion Correlation Enhanced Empathetic Dialogue Generation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 10568–10586, Singapore. Association for Computational Linguistics.