论文标题
Dialoguetrm：探索对话中的内部和模式间情感行为
DialogueTRM: Exploring the Intra- and Inter-Modal Emotional Behaviors in the Conversation
论文作者
论文摘要
对话中的情绪识别（ERC）对于构建善解人意的人机系统至关重要。但是，现有关于ERC的研究主要集中于总结对话中的上下文信息，但是，忽略了不同方式内部和跨不同方式的差异性情绪行为。设计适合差异化多模式情感行为的适当策略可以产生更准确的情感预测。因此，我们提出了Dialoguetransformer，以探索从模式内和模式间观点探索差异化的情感行为。对于模式内，我们构建了一个新型的分层变压器，该变压器可以根据每种模态内的差异上下文偏好轻松地在顺序和前馈结构之间切换。对于模式间，我们构成了一种新型的多层次交互式融合，它同时应用神经元和矢量粒度的特征相互作用来学习所有模态的差异贡献。实验结果表明，DialoguetrM在三个基准数据集上的优于最先进的东西。
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.
