DialogueTRM: Exploring the Intra- and Inter-Modal Emotional Behaviors in the Conversation

• URL: http://arxiv.org/abs/2010.07637v1
• Date: Thu, 15 Oct 2020 10:10:41 GMT
• Title: DialogueTRM: Exploring the Intra- and Inter-Modal Emotional Behaviors in the Conversation
• Authors: Yuzhao Mao, Qi Sun, Guang Liu, Xiaojie Wang, Weiguo Gao, Xuan Li, Jianping Shen
• Abstract summary: We propose the DialogueTransformer to explore the differentiated emotional behaviors from the intra- and inter-modal perspectives. For intra-modal, we construct a novel Hierarchical Transformer that can easily switch between sequential and feed-forward structures. For inter-modal, we constitute a novel Multi-Grained Interactive Fusion that applies both neuron- and vector-grained feature interactions.
• Score: 20.691806885663848
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Emotion Recognition in Conversations (ERC) is essential for building empathetic human-machine systems. Existing studies on ERC primarily focus on summarizing the context information in a conversation, however, ignoring the differentiated emotional behaviors within and across different modalities. Designing appropriate strategies that fit the differentiated multi-modal emotional behaviors can produce more accurate emotional predictions. Thus, we propose the DialogueTransformer to explore the differentiated emotional behaviors from the intra- and inter-modal perspectives. For intra-modal, we construct a novel Hierarchical Transformer that can easily switch between sequential and feed-forward structures according to the differentiated context preference within each modality. For inter-modal, we constitute a novel Multi-Grained Interactive Fusion that applies both neuron- and vector-grained feature interactions to learn the differentiated contributions across all modalities. Experimental results show that DialogueTRM outperforms the state-of-the-art by a significant margin on three benchmark datasets.

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