Wish I can feel what you feel: a neural approach for empathetic response generation

Yangbin Chen, Chufeng Liang

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

1 Citation (Scopus)

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.
Original languageEnglish
Title of host publicationFindings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Pages922-933
DOIs
Publication statusPublished - 2022
Externally publishedYes

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