An Iterative Emotion Interaction Network for Emotion Recognition in Conversations

Xin Lu, Yanyan Zhao, Yang Wu, Yijian Tian, Huipeng Chen, Bing Qin

Abstract
Emotion recognition in conversations (ERC) has received much attention recently in the natural language processing community. Considering that the emotions of the utterances in conversations are interactive, previous works usually implicitly model the emotion interaction between utterances by modeling dialogue context, but the misleading emotion information from context often interferes with the emotion interaction. We noticed that the gold emotion labels of the context utterances can provide explicit and accurate emotion interaction, but it is impossible to input gold labels at inference time. To address this problem, we propose an iterative emotion interaction network, which uses iteratively predicted emotion labels instead of gold emotion labels to explicitly model the emotion interaction. This approach solves the above problem, and can effectively retain the performance advantages of explicit modeling. We conduct experiments on two datasets, and our approach achieves state-of-the-art performance.
Anthology ID:
2020.coling-main.360
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Editors:
Donia Scott, Nuria Bel, Chengqing Zong
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
4078–4088
Language:
URL:
https://aclanthology.org/2020.coling-main.360
DOI:
10.18653/v1/2020.coling-main.360
Bibkey:
Cite (ACL):
Xin Lu, Yanyan Zhao, Yang Wu, Yijian Tian, Huipeng Chen, and Bing Qin. 2020. An Iterative Emotion Interaction Network for Emotion Recognition in Conversations. In Proceedings of the 28th International Conference on Computational Linguistics, pages 4078–4088, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
An Iterative Emotion Interaction Network for Emotion Recognition in Conversations (Lu et al., COLING 2020)
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PDF:
https://preview.aclanthology.org/nschneid-patch-4/2020.coling-main.360.pdf
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