A Transformer-Based Model With Self-Distillation for Multimodal Emotion Recognition in Conversations

• URL: http://arxiv.org/abs/2310.20494v1
• Date: Tue, 31 Oct 2023 14:33:30 GMT
• Title: A Transformer-Based Model With Self-Distillation for Multimodal Emotion Recognition in Conversations
• Authors: Hui Ma, Jian Wang, Hongfei Lin, Bo Zhang, Yijia Zhang, Bo Xu
• Abstract summary: We propose a transformer-based model with self-distillation (SDT) for the task. The proposed model captures intra- and inter-modal interactions by utilizing intra- and inter-modal transformers. We introduce self-distillation to transfer knowledge of hard and soft labels from the proposed model to each modality.
• Score: 15.77747948751497
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Emotion recognition in conversations (ERC), the task of recognizing the emotion of each utterance in a conversation, is crucial for building empathetic machines. Existing studies focus mainly on capturing context- and speaker-sensitive dependencies on the textual modality but ignore the significance of multimodal information. Different from emotion recognition in textual conversations, capturing intra- and inter-modal interactions between utterances, learning weights between different modalities, and enhancing modal representations play important roles in multimodal ERC. In this paper, we propose a transformer-based model with self-distillation (SDT) for the task. The transformer-based model captures intra- and inter-modal interactions by utilizing intra- and inter-modal transformers, and learns weights between modalities dynamically by designing a hierarchical gated fusion strategy. Furthermore, to learn more expressive modal representations, we treat soft labels of the proposed model as extra training supervision. Specifically, we introduce self-distillation to transfer knowledge of hard and soft labels from the proposed model to each modality. Experiments on IEMOCAP and MELD datasets demonstrate that SDT outperforms previous state-of-the-art baselines.

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