  The main task of Multimodal Emotion Recognition in Conversations (MERC) is to
identify the emotions in modalities, e.g., text, audio, image and video, which
is a significant development direction for realizing machine intelligence.
However, many data in MERC naturally exhibit an imbalanced distribution of
emotion categories, and researchers ignore the negative impact of imbalanced
data on emotion recognition. To tackle this problem, we systematically analyze
it from three aspects: data augmentation, loss sensitivity, and sampling
strategy, and propose the Class Boundary Enhanced Representation Learning
(CBERL) model. Concretely, we first design a multimodal generative adversarial
network to address the imbalanced distribution of {emotion} categories in raw
data. Secondly, a deep joint variational autoencoder is proposed to fuse
complementary semantic information across modalities and obtain discriminative
feature representations. Finally, we implement a multi-task graph neural
network with mask reconstruction and classification optimization to solve the
problem of overfitting and underfitting in class boundary learning, and achieve
cross-modal emotion recognition. We have conducted extensive experiments on the
IEMOCAP and MELD benchmark datasets, and the results show that CBERL has
achieved a certain performance improvement in the effectiveness of emotion
recognition. Especially on the minority class fear and disgust emotion labels,
our model improves the accuracy and F1 value by 10% to 20%.
