Enhancing Emotion Recognition in Conversation through Emotional Cross-Modal Fusion and Inter-class Contrastive Learning

• URL: http://arxiv.org/abs/2405.17900v1
• Date: Tue, 28 May 2024 07:22:30 GMT
• Title: Enhancing Emotion Recognition in Conversation through Emotional Cross-Modal Fusion and Inter-class Contrastive Learning
• Authors: Haoxiang Shi, Xulong Zhang, Ning Cheng, Yong Zhang, Jun Yu, Jing Xiao, Jianzong Wang,
• Abstract summary: The purpose of emotion recognition in conversation (ERC) is to identify the emotion category of an utterance based on contextual information. Previous ERC methods relied on simple connections for cross-modal fusion. We propose a cross-modal fusion emotion prediction network based on vector connections.
• Score: 40.101313334772016
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The purpose of emotion recognition in conversation (ERC) is to identify the emotion category of an utterance based on contextual information. Previous ERC methods relied on simple connections for cross-modal fusion and ignored the information differences between modalities, resulting in the model being unable to focus on modality-specific emotional information. At the same time, the shared information between modalities was not processed to generate emotions. Information redundancy problem. To overcome these limitations, we propose a cross-modal fusion emotion prediction network based on vector connections. The network mainly includes two stages: the multi-modal feature fusion stage based on connection vectors and the emotion classification stage based on fused features. Furthermore, we design a supervised inter-class contrastive learning module based on emotion labels. Experimental results confirm the effectiveness of the proposed method, demonstrating excellent performance on the IEMOCAP and MELD datasets.

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