论文标题
聊天聊天：用于对话级情绪分析的分层胶囊
Chat-Capsule: A Hierarchical Capsule for Dialog-level Emotion Analysis
论文作者
论文摘要
对话情绪分析的许多研究仅集中在话语级别的情感上。因此，这些模型未针对对话级的情绪检测进行优化，即预测整个对话的情绪类别。更重要的是，这些模型无法从整个对话框提供的上下文中受益。在现实世界应用中，对话的注释可以细化，包括话语级标签（例如扬声器类型，意图类别和情感类别）和对话级标签（例如用户满意度和情感曲线类别）。在本文中，我们提出了一个基于上下文的分层注意胶囊〜（聊天式）模型，该模型既建模话语级别和对话级情感及其相互关系。在从电子商务平台的客户支持中收集的对话框数据集中，我们的模型还能够预测用户满意度和情感曲线类别。情绪曲线是指随着对话的发展的变化。实验表明，在基准数据集和专有数据集上，提出的聊天聊天量优于最先进的基线。源代码将在接受后发布。
Many studies on dialog emotion analysis focus on utterance-level emotion only. These models hence are not optimized for dialog-level emotion detection, i.e. to predict the emotion category of a dialog as a whole. More importantly, these models cannot benefit from the context provided by the whole dialog. In real-world applications, annotations to dialog could fine-grained, including both utterance-level tags (e.g. speaker type, intent category, and emotion category), and dialog-level tags (e.g. user satisfaction, and emotion curve category). In this paper, we propose a Context-based Hierarchical Attention Capsule~(Chat-Capsule) model, which models both utterance-level and dialog-level emotions and their interrelations. On a dialog dataset collected from customer support of an e-commerce platform, our model is also able to predict user satisfaction and emotion curve category. Emotion curve refers to the change of emotions along the development of a conversation. Experiments show that the proposed Chat-Capsule outperform state-of-the-art baselines on both benchmark dataset and proprietary dataset. Source code will be released upon acceptance.
