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arXiv · 2609.29556

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

Jiaqi Qiao·Yifan Lyu·Xiujuan Xu
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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.

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Jiaqi Qiao, Yifan Lyu, Xiujuan Xu. 2026-09-02. Cross-Modal Emotion Understanding: A Transformer-GAT Approach for Dialogue Emotion Recognition. https://arxiv.org/abs/2609.29556

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