  Emotion Recognition in Conversations (ERC) has considerable prospects for
developing empathetic machines. For multimodal ERC, it is vital to understand
context and fuse modality information in conversations. Recent graph-based
fusion methods generally aggregate multimodal information by exploring unimodal
and cross-modal interactions in a graph. However, they accumulate redundant
information at each layer, limiting the context understanding between
modalities. In this paper, we propose a novel Multimodal Dynamic Fusion Network
(MM-DFN) to recognize emotions by fully understanding multimodal conversational
context. Specifically, we design a new graph-based dynamic fusion module to
fuse multimodal contextual features in a conversation. The module reduces
redundancy and enhances complementarity between modalities by capturing the
dynamics of contextual information in different semantic spaces. Extensive
experiments on two public benchmark datasets demonstrate the effectiveness and
superiority of MM-DFN.
