Cross-Modal Emotion Understanding: A Transformer-GAT Approach for Dialogue Emotion Recognition

Abstract:Multimodal emotion recognition is a key research area in affective computing, with applications in sentiment analysis, intelligent customer service, and human-computer interaction. However, existing methods often rely on single-modal features or simple multimodal fusion, failing to capture the synergy between global and local contexts, which limits model performance and emotion understanding. To address this challenge, we propose Transformer-GAT, a hybrid framework that combines Transformer and the Graph Attention Network to enable cross-modal emotion understanding. The Transformer is used to capture global semantic information, while the Graph Attention Network is employed to model fine-grained relationships between modalities, thereby enhancing the representation of emotional features. Experiments on the IEMOCAP and MELD datasets show that our model achieves weighted F1 scores of 72.45% and 77.37%, outperforming state-of-the-art methods. These results demonstrate that Transformer-GAT effectively integrates multimodal features, balances global and local contexts, and provides deeper emotional insights, offering new directions for multimodal emotion computing.

Artificial Intelligence (cs.AI)

arXiv:2609.29556 [cs.AI]

(or arXiv:2609.29556v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.29556 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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