MISC: A Mixed Strategy-Aware Model integrating COMET for Emotional Support Conversation

Quan Tu, Yanran Li, Jianwei Cui, Bin Wang, Ji-Rong Wen, Rui Yan


Abstract
Applying existing methods to emotional support conversation—which provides valuable assistance to people who are in need—has two major limitations: (a) they generally employ a conversation-level emotion label, which is too coarse-grained to capture user’s instant mental state; (b) most of them focus on expressing empathy in the response(s) rather than gradually reducing user’s distress. To address the problems, we propose a novel model \textbf{MISC}, which firstly infers the user’s fine-grained emotional status, and then responds skillfully using a mixture of strategy. Experimental results on the benchmark dataset demonstrate the effectiveness of our method and reveal the benefits of fine-grained emotion understanding as well as mixed-up strategy modeling.
Anthology ID:
2022.acl-long.25
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:
308–319
Language:
URL:
https://aclanthology.org/2022.acl-long.25
DOI:
10.18653/v1/2022.acl-long.25
Bibkey:
Cite (ACL):
Quan Tu, Yanran Li, Jianwei Cui, Bin Wang, Ji-Rong Wen, and Rui Yan. 2022. MISC: A Mixed Strategy-Aware Model integrating COMET for Emotional Support Conversation. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 308–319, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
MISC: A Mixed Strategy-Aware Model integrating COMET for Emotional Support Conversation (Tu et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.25.pdf
Code
 morecry/misc
Data
ATOMICConceptNet