Improving Contrastive Learning in Emotion Recognition in Conversation via Data Augmentation and Decoupled Neutral Emotion

Yujin Kang, Yoon-Sik Cho


Abstract
Emotion recognition in conversation (ERC) has attracted much attention due to its wide applications. While consistent improvement is being made in this area, inevitable challenge comes from the dataset. The ERC dataset exhibits significantly imbalanced emotion distribution. While the utterances with neutral emotion predominate the data, this emotion label is always treated the same as other emotion labels in current approaches. To address the problem caused by the dataset, we propose a supervised contrastive learning specifically oriented for ERC task. We employ a novel data augmentation method emulating the emotion dynamics in a conversation and formulate supervised contrastive learning method tailored for ERC addressing the predominance and the ambiguity of neutral emotion. Experimental results on four benchmark datasets demonstrate the effectiveness of our approach.
Anthology ID:
2024.eacl-long.134
Volume:
Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
March
Year:
2024
Address:
St. Julian’s, Malta
Editors:
Yvette Graham, Matthew Purver
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2194–2208
Language:
URL:
https://aclanthology.org/2024.eacl-long.134
DOI:
Bibkey:
Cite (ACL):
Yujin Kang and Yoon-Sik Cho. 2024. Improving Contrastive Learning in Emotion Recognition in Conversation via Data Augmentation and Decoupled Neutral Emotion. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2194–2208, St. Julian’s, Malta. Association for Computational Linguistics.
Cite (Informal):
Improving Contrastive Learning in Emotion Recognition in Conversation via Data Augmentation and Decoupled Neutral Emotion (Kang & Cho, EACL 2024)
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PDF:
https://aclanthology.org/2024.eacl-long.134.pdf
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 https://aclanthology.org/2024.eacl-long.134.mp4