Emp-RFT: Empathetic Response Generation via Recognizing Feature Transitions between Utterances

Wongyu Kim, Youbin Ahn, Donghyun Kim, Kyong-Ho Lee


Abstract
Each utterance in multi-turn empathetic dialogues has features such as emotion, keywords, and utterance-level meaning. Feature transitions between utterances occur naturally. However, existing approaches fail to perceive the transitions because they extract features for the context at the coarse-grained level. To solve the above issue, we propose a novel approach of recognizing feature transitions between utterances, which helps understand the dialogue flow and better grasp the features of utterance that needs attention. Also, we introduce a response generation strategy to help focus on emotion and keywords related to appropriate features when generating responses. Experimental results show that our approach outperforms baselines and especially, achieves significant improvements on multi-turn dialogues.
Anthology ID:
2022.naacl-main.303
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4118–4128
Language:
URL:
https://aclanthology.org/2022.naacl-main.303
DOI:
10.18653/v1/2022.naacl-main.303
Bibkey:
Cite (ACL):
Wongyu Kim, Youbin Ahn, Donghyun Kim, and Kyong-Ho Lee. 2022. Emp-RFT: Empathetic Response Generation via Recognizing Feature Transitions between Utterances. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4118–4128, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Emp-RFT: Empathetic Response Generation via Recognizing Feature Transitions between Utterances (Kim et al., NAACL 2022)
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PDF:
https://aclanthology.org/2022.naacl-main.303.pdf