Multimodal Knowledge-enhanced Interactive Network with Mixed Contrastive Learning for Emotion Recognition in Conversation

Emotion Recognition in Conversations (ERC) aims to accurately identify the emotional labels of each utterance in a conversation, holding significant application value in human-computer interaction. Existing research suggests introducing commonsense knowledge (CSK) and multimodal information enhances model performance in ERC tasks. However, several challenges persist: (1) the neglect of complex psychological influences between utterances; (2) noise issues within modal information; (3) prediction challenges for emotion labels with few samples in different categories that exhibit semantic similarity but distinct emotional categories. To address the above problems, we propose a Multimodal Knowledge -enhanced Interactive Network with Mixed Contrastive Learning (MKIN-MCL). Firstly, we establish a knowledge aggregation graph to capture the dependencies of commonsense knowledge (CSK) between utterances during a conversation. We actively aggregate relevant knowledge information to enhance text features. Simultaneously, we apply feature filters for acoustic and visual modalities to eliminate noise and enhance feature quality. Furthermore, we implement an interactive attention module by stacking designed Cross -modal Interactive Transformers (CITs) to continuously explore the relevance between the interacting parties in their respective semantic spaces, thus improving the effectiveness of modality interaction while reducing noise generated during the interaction. Lastly, we employ the Mixed Contrastive Learning (MCL) strategy to enhance the model's ability to handle few -shot labels. This strategy utilizes unsupervised contrastive learning to improve the representation capability of the multimodal fusion features and supervised contrastive learning to extract information from few -shot labels. Extensive experiments on two benchmark datasets, IEMOCAP and MELD, validate the effectiveness and superiority of the proposed model.