MuCDN: Mutual Conversational Detachment Network for Emotion Recognition in Multi-Party Conversations

Weixiang Zhao, Yanyan Zhao, Bing Qin


Abstract
As an emerging research topic in natural language processing community, emotion recognition in multi-party conversations has attained increasing interest. Previous approaches that focus either on dyadic or multi-party scenarios exert much effort to cope with the challenge of emotional dynamics and achieve appealing results. However, since emotional interactions among speakers are often more complicated within the entangled multi-party conversations, these works are limited in capturing effective emotional clues in conversational context. In this work, we propose Mutual Conversational Detachment Network (MuCDN) to clearly and effectively understand the conversational context by separating conversations into detached threads. Specifically, two detachment ways are devised to perform context and speaker-specific modeling within detached threads and they are bridged through a mutual module. Experimental results on two datasets show that our model achieves better performance over the baseline models.
Anthology ID:
2022.coling-1.612
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
7020–7030
Language:
URL:
https://aclanthology.org/2022.coling-1.612
DOI:
Bibkey:
Cite (ACL):
Weixiang Zhao, Yanyan Zhao, and Bing Qin. 2022. MuCDN: Mutual Conversational Detachment Network for Emotion Recognition in Multi-Party Conversations. In Proceedings of the 29th International Conference on Computational Linguistics, pages 7020–7030, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
MuCDN: Mutual Conversational Detachment Network for Emotion Recognition in Multi-Party Conversations (Zhao et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.612.pdf
Code
 circle-hit/mucdn
Data
EmoryNLPMELD