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Emotion Recognition in Conversations (ERC) has considerable prospects fordeveloping empathetic machines. For multimodal ERC, it is vital to understandcontext and fuse modality information in conversations. Recent graph-basedfusion methods generally aggregate multimodal information by exploring unimodaland cross-modal interactions in a graph. However, they accumulate redundantinformation at each layer, limiting the context understanding betweenmodalities. In this paper, we propose a novel Multimodal Dynamic Fusion Network(MM-DFN) to recognize emotions by fully understanding multimodal conversationalcontext. Specifically, we design a new graph-based dynamic fusion module tofuse multimodal contextual features in a conversation. The module reducesredundancy and enhances complementarity between modalities by capturing thedynamics of contextual information in different semantic spaces. Extensiveexperiments on two public benchmark datasets demonstrate the effectiveness andsuperiority of MM-DFN.

MM-DFN: Multimodal Dynamic Fusion Network for Emotion Recognition in Conversations