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Emotion Recognition in Conversation (ERC) plays an important role in drivingthe development of human-machine interaction. Emotions can exist in multiplemodalities, and multimodal ERC mainly faces two problems: (1) the noise problemin the cross-modal information fusion process, and (2) the prediction problemof less sample emotion labels that are semantically similar but differentcategories. To address these issues and fully utilize the features of eachmodality, we adopted the following strategies: first, deep emotion cuesextraction was performed on modalities with strong representation ability, andfeature filters were designed as multimodal prompt information for modalitieswith weak representation ability. Then, we designed a Multimodal PromptTransformer (MPT) to perform cross-modal information fusion. MPT embedsmultimodal fusion information into each attention layer of the Transformer,allowing prompt information to participate in encoding textual features andbeing fused with multi-level textual information to obtain better multimodalfusion features. Finally, we used the Hybrid Contrastive Learning (HCL)strategy to optimize the model's ability to handle labels with few samples.This strategy uses unsupervised contrastive learning to improve therepresentation ability of multimodal fusion and supervised contrastive learningto mine the information of labels with few samples. Experimental results showthat our proposed model outperforms state-of-the-art models in ERC on twobenchmark datasets.

Multimodal Prompt Transformer with Hybrid Contrastive Learning for Emotion Recognition in Conversation