  Emotion Recognition in Conversation (ERC) plays a significant part in
Human-Computer Interaction (HCI) systems since it can provide empathetic
services. Multimodal ERC can mitigate the drawbacks of uni-modal approaches.
Recently, Graph Neural Networks (GNNs) have been widely used in a variety of
fields due to their superior performance in relation modeling. In multimodal
ERC, GNNs are capable of extracting both long-distance contextual information
and inter-modal interactive information. Unfortunately, since existing methods
such as MMGCN directly fuse multiple modalities, redundant information may be
generated and diverse information may be lost. In this work, we present a
directed Graph based Cross-modal Feature Complementation (GraphCFC) module that
can efficiently model contextual and interactive information. GraphCFC
alleviates the problem of heterogeneity gap in multimodal fusion by utilizing
multiple subspace extractors and Pair-wise Cross-modal Complementary (PairCC)
strategy. We extract various types of edges from the constructed graph for
encoding, thus enabling GNNs to extract crucial contextual and interactive
information more accurately when performing message passing. Furthermore, we
design a GNN structure called GAT-MLP, which can provide a new unified network
framework for multimodal learning. The experimental results on two benchmark
datasets show that our GraphCFC outperforms the state-of-the-art (SOTA)
approaches.
