@inproceedings{zhao-etal-2022-mucdn,
title = "{M}u{CDN}: Mutual Conversational Detachment Network for Emotion Recognition in Multi-Party Conversations",
author = "Zhao, Weixiang and
Zhao, Yanyan and
Qin, Bing",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.612",
pages = "7020--7030",
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.",
}
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%0 Conference Proceedings
%T MuCDN: Mutual Conversational Detachment Network for Emotion Recognition in Multi-Party Conversations
%A Zhao, Weixiang
%A Zhao, Yanyan
%A Qin, Bing
%S Proceedings of the 29th International Conference on Computational Linguistics
%D 2022
%8 October
%I International Committee on Computational Linguistics
%C Gyeongju, Republic of Korea
%F zhao-etal-2022-mucdn
%X 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.
%U https://aclanthology.org/2022.coling-1.612
%P 7020-7030
Markdown (Informal)
[MuCDN: Mutual Conversational Detachment Network for Emotion Recognition in Multi-Party Conversations](https://aclanthology.org/2022.coling-1.612) (Zhao et al., COLING 2022)
ACL