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        <title>Wish I Can Feel What You Feel: A Neural Approach for Empathetic Response Generation</title>
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        <namePart type="given">Yangbin</namePart>
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            <namePart type="given">Yue</namePart>
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    <abstract>Expressing empathy is important in everyday conversations, and exploring how empathy arises is crucial in automatic response generation. Most previous approaches consider only a single factor that affects empathy. However, in practice, empathy generation and expression is a very complex and dynamic psychological process. A listener needs to find out events which cause a speaker’s emotions (emotion cause extraction), project the events into some experience (knowledge extension), and express empathy in the most appropriate way (communication mechanism).To this end, we propose a novel approach, which integrates the three components - emotion cause, knowledge graph, and communication mechanism for empathetic response generation. Experimental results on the benchmark dataset demonstrate the effectiveness of our method and show that incorporating the key components generates more informative and empathetic responses.</abstract>
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    <identifier type="doi">10.18653/v1/2022.findings-emnlp.65</identifier>
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%0 Conference Proceedings
%T Wish I Can Feel What You Feel: A Neural Approach for Empathetic Response Generation
%A Chen, Yangbin
%A Liang, Chunfeng
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F chen-liang-2022-wish
%X Expressing empathy is important in everyday conversations, and exploring how empathy arises is crucial in automatic response generation. Most previous approaches consider only a single factor that affects empathy. However, in practice, empathy generation and expression is a very complex and dynamic psychological process. A listener needs to find out events which cause a speaker’s emotions (emotion cause extraction), project the events into some experience (knowledge extension), and express empathy in the most appropriate way (communication mechanism).To this end, we propose a novel approach, which integrates the three components - emotion cause, knowledge graph, and communication mechanism for empathetic response generation. Experimental results on the benchmark dataset demonstrate the effectiveness of our method and show that incorporating the key components generates more informative and empathetic responses.
%R 10.18653/v1/2022.findings-emnlp.65
%U https://aclanthology.org/2022.findings-emnlp.65
%U https://doi.org/10.18653/v1/2022.findings-emnlp.65
%P 922-933

Markdown (Informal)

[Wish I Can Feel What You Feel: A Neural Approach for Empathetic Response Generation](https://aclanthology.org/2022.findings-emnlp.65) (Chen & Liang, Findings 2022)

• Wish I Can Feel What You Feel: A Neural Approach for Empathetic Response Generation (Chen & Liang, Findings 2022)

ACL

• Yangbin Chen and Chunfeng Liang. 2022. Wish I Can Feel What You Feel: A Neural Approach for Empathetic Response Generation. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 922–933, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.