Revisiting Multimodal Emotion Recognition in Conversation from the Perspective of Graph Spectrum

Efficiently capturing consistent and complementary semantic features in context is crucial for Multimodal Emotion Recognition in Conversations (MERC). However, limited by the over-smoothing or low-pass filtering characteristics of spatial graph neural networks, are insufficient to accurately capture the long-distance consistency low-frequency information and complementarity high-frequency information of the utterances. To this end, this paper revisits the task of MERC from the perspective of the graph spectrum and proposes a Graph-Spectrum-based Multimodal Consistency and Complementary collaborative learning framework GS-MCC. First, GS-MCC uses a sliding window to construct a multimodal interaction graph to model conversational relationships and designs efficient Fourier graph operators (FGO) to extract long-distance high-frequency and low-frequency information, respectively. FGO can be stacked in multiple layers, which can effectively alleviate the over-smoothing problem. Then, GS-MCC uses contrastive learning to construct self-supervised signals that reflect complementarity and consistent semantic collaboration with high and low-frequency signals, thereby improving the ability of high and low-frequency information to reflect genuine emotions. Finally, GS-MCC inputs the coordinated high and low-frequency information into the MLP network and softmax function for emotion prediction. Extensive experiments have proven the superiority of the GS-MCC architecture proposed in this paper on two benchmark data sets.