论文标题
emocaps：基于情感胶囊的对话情感识别的模型
EmoCaps: Emotion Capsule based Model for Conversational Emotion Recognition
论文作者
论文摘要
对话中的情感识别（ERC）旨在分析说话者的状态并确定他们在对话中的情感。 ERC中的最新作品集中于上下文建模，但忽略了上下文情感倾向的表示。为了有效地提取多模式信息和话语的情感趋势，我们提出了一种名为Emoformer的新结构，以从不同方式中提取多模式情感向量，并将它们与句子向量融合为情感胶囊。此外，我们设计了一种称为emocaps的端到端ERC模型，该模型通过符号结构提取情绪向量，并从上下文分析模型中获得情感分类。通过使用两个基准数据集的实验，我们的模型比现有的最新模型显示出更好的性能。
Emotion recognition in conversation (ERC) aims to analyze the speaker's state and identify their emotion in the conversation. Recent works in ERC focus on context modeling but ignore the representation of contextual emotional tendency. In order to extract multi-modal information and the emotional tendency of the utterance effectively, we propose a new structure named Emoformer to extract multi-modal emotion vectors from different modalities and fuse them with sentence vector to be an emotion capsule. Furthermore, we design an end-to-end ERC model called EmoCaps, which extracts emotion vectors through the Emoformer structure and obtain the emotion classification results from a context analysis model. Through the experiments with two benchmark datasets, our model shows better performance than the existing state-of-the-art models.
