CASE: Aligning Coarse-to-Fine Cognition and Affection for Empathetic Response Generation
Jinfeng Zhou, Chujie Zheng, Bo Wang, Zheng Zhang, Minlie Huang
Abstract
Empathetic conversation is psychologically supposed to be the result of conscious alignment and interaction between the cognition and affection of empathy. However, existing empathetic dialogue models usually consider only the affective aspect or treat cognition and affection in isolation, which limits the capability of empathetic response generation. In this work, we propose the CASE model for empathetic dialogue generation. It first builds upon a commonsense cognition graph and an emotional concept graph and then aligns the user’s cognition and affection at both the coarse-grained and fine-grained levels. Through automatic and manual evaluation, we demonstrate that CASE outperforms state-of-the-art baselines of empathetic dialogues and can generate more empathetic and informative responses.- Anthology ID:
- 2023.acl-long.457
- Volume:
- Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 8223–8237
- Language:
- URL:
- https://aclanthology.org/2023.acl-long.457
- DOI:
- 10.18653/v1/2023.acl-long.457
- Cite (ACL):
- Jinfeng Zhou, Chujie Zheng, Bo Wang, Zheng Zhang, and Minlie Huang. 2023. CASE: Aligning Coarse-to-Fine Cognition and Affection for Empathetic Response Generation. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 8223–8237, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- CASE: Aligning Coarse-to-Fine Cognition and Affection for Empathetic Response Generation (Zhou et al., ACL 2023)
- PDF:
- https://preview.aclanthology.org/naacl-24-ws-corrections/2023.acl-long.457.pdf