Multi-Party Empathetic Dialogue Generation: A New Task for Dialog Systems

Ling.Yu Zhu, Zhengkun Zhang, Jun Wang, Hongbin Wang, Haiying Wu, Zhenglu Yang


Abstract
Empathetic dialogue assembles emotion understanding, feeling projection, and appropriate response generation. Existing work for empathetic dialogue generation concentrates on the two-party conversation scenario. Multi-party dialogues, however, are pervasive in reality. Furthermore, emotion and sensibility are typically confused; a refined empathy analysis is needed for comprehending fragile and nuanced human feelings. We address these issues by proposing a novel task called Multi-Party Empathetic Dialogue Generation in this study. Additionally, a Static-Dynamic model for Multi-Party Empathetic Dialogue Generation, SDMPED, is introduced as a baseline by exploring the static sensibility and dynamic emotion for the multi-party empathetic dialogue learning, the aspects that help SDMPED achieve the state-of-the-art performance.
Anthology ID:
2022.acl-long.24
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
298–307
Language:
URL:
https://aclanthology.org/2022.acl-long.24
DOI:
10.18653/v1/2022.acl-long.24
Bibkey:
Cite (ACL):
Ling.Yu Zhu, Zhengkun Zhang, Jun Wang, Hongbin Wang, Haiying Wu, and Zhenglu Yang. 2022. Multi-Party Empathetic Dialogue Generation: A New Task for Dialog Systems. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 298–307, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Multi-Party Empathetic Dialogue Generation: A New Task for Dialog Systems (Zhu et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.24.pdf
Data
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