Enhancing Emotion Recognition in Conversation through Emotional Cross-Modal Fusion and Inter-class Contrastive Learning
<|reference_start|>Enhancing Emotion Recognition in Conversation through Emotional Cross-Modal Fusion and Inter-class Contrastive Learning: 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.<|reference_end|>
@article{shi2024enhancing,
    title={Enhancing Emotion Recognition in Conversation through Emotional
  Cross-Modal Fusion and Inter-class Contrastive Learning},
    author={Haoxiang Shi, Xulong Zhang, Ning Cheng, Yong Zhang, Jun Yu, Jing Xiao
  and Jianzong Wang},
    journal={arXiv preprint arXiv:2405.17900},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.17900},
    primaryClass={cs.CL}
}