Don’t Lose Yourself! Empathetic Response Generation via Explicit Self-Other Awareness

Weixiang Zhao, Yanyan Zhao, Xin Lu, Bing Qin


Abstract
As a critical step to achieve human-like chatbots, empathetic response generation has attained increasing interests. Previous attempts are incomplete and not sufficient enough to elicit empathy because they only stay on the initial stage of empathy to automatically sense and simulate the feelings and thoughts of others via other-awareness. However, they ignore to include self-awareness to consider the own views of the self in their responses, which is a crucial process to achieve the empathy. To this end, we propose to generate Empathetic response with explicit Self-Other Awareness (EmpSOA). Specifically, three stages, self-other differentiation, self-other modulation and self-other generation, are devised to clearly maintain, regulate and inject the self-other aware information into the process of empathetic response generation. Both automatic and human evaluations on the benchmark dataset demonstrate the superiority of EmpSOA to generate more empathetic responses. Our source code will be publicly available.
Anthology ID:
2023.findings-acl.843
Volume:
Findings of the Association for Computational Linguistics: ACL 2023
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
13331–13344
Language:
URL:
https://aclanthology.org/2023.findings-acl.843
DOI:
10.18653/v1/2023.findings-acl.843
Bibkey:
Cite (ACL):
Weixiang Zhao, Yanyan Zhao, Xin Lu, and Bing Qin. 2023. Don’t Lose Yourself! Empathetic Response Generation via Explicit Self-Other Awareness. In Findings of the Association for Computational Linguistics: ACL 2023, pages 13331–13344, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
Don’t Lose Yourself! Empathetic Response Generation via Explicit Self-Other Awareness (Zhao et al., Findings 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.findings-acl.843.pdf