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With the continuous development of deep learning (DL), the task of multimodaldialogue emotion recognition (MDER) has recently received extensive researchattention, which is also an essential branch of DL. The MDER aims to identifythe emotional information contained in different modalities, e.g., text, video,and audio, in different dialogue scenes. However, existing research has focusedon modeling contextual semantic information and dialogue relations betweenspeakers while ignoring the impact of event relations on emotion. To tackle theabove issues, we propose a novel Dialogue and Event Relation-Aware GraphConvolutional Neural Network for Multimodal Emotion Recognition (DER-GCN)method. It models dialogue relations between speakers and captures latent eventrelations information. Specifically, we construct a weighted multi-relationshipgraph to simultaneously capture the dependencies between speakers and eventrelations in a dialogue. Moreover, we also introduce a Self-Supervised MaskedGraph Autoencoder (SMGAE) to improve the fusion representation ability offeatures and structures. Next, we design a new Multiple Information Transformer(MIT) to capture the correlation between different relations, which can providea better fuse of the multivariate information between relations. Finally, wepropose a loss optimization strategy based on contrastive learning to enhancethe representation learning ability of minority class features. We conductextensive experiments on the IEMOCAP and MELD benchmark datasets, which verifythe effectiveness of the DER-GCN model. The results demonstrate that our modelsignificantly improves both the average accuracy and the f1 value of emotionrecognition.

DER-GCN: Dialogue and Event Relation-Aware Graph Convolutional Neural Network for Multimodal Dialogue Emotion Recognition