Papers › EmoCaps: Emotion Capsule based Model for Conversational Emotion Recognition

EmoCaps: Emotion Capsule based Model for Conversational Emotion Recognition

25 Mar 2022Findings (ACL) 2022 5arXiv:2203.13504archive 2025-07-28

Zaijing Li, Fengxiao Tang, Ming Zhao, Yusen Zhu

Emotion recognition in conversation (ERC) aims to analyze the speaker's state and identify their emotion in the conversation. Recent works in ERC focus on context modeling but ignore the representation of contextual emotional tendency. In order to extract multi-modal information and the emotional tendency of the utterance effectively, we propose a new structure named Emoformer to extract multi-modal emotion vectors from different modalities and fuse them with sentence vector to be an emotion capsule. Furthermore, we design an end-to-end ERC model called EmoCaps, which extracts emotion vectors through the Emoformer structure and obtain the emotion classification results from a context analysis model. Through the experiments with two benchmark datasets, our model shows better performance than the existing state-of-the-art models.

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Tasks

Emotion ClassificationEmotion RecognitionEmotion Recognition in ConversationSentence

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Emotion Recognition in Conversation IEMOCAP EmoCaps Weighted-F1 71.77 #6 of 59 Archive leaderboard report
Emotion Recognition in Conversation MELD EmoCaps Weighted-F1 64.00 #41 of 68 Archive leaderboard report
Emotion Recognition in Conversation MELD EmoCaps-Text Weighted-F1 63.51 #43 of 68 Archive leaderboard report

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