Empathy is an important characteristic to be considered when building a more intelligent and humanized dialogue agent. However, existing methods did not fully comprehend empathy as a complex process involving three aspects: cognition, affection and behavior. In this paper, we propose CAB, a novel framework that takes a comprehensive perspective of cognition, affection and behavior to generate empathetic responses. For cognition, we build paths between critical keywords in the dialogue by leveraging external knowledge. This is because keywords in a dialogue are the core of sentences. Building the logic relationship between keywords, which is overlooked by the majority of existing works, can improve the understanding of keywords and contextual logic, thus enhance the cognitive ability. For affection, we capture the emotional dependencies with dual latent variables that contain both interlocutors' emotions. The reason is that considering both interlocutors' emotions simultaneously helps to learn the emotional dependencies. For behavior, we use appropriate dialogue acts to guide the dialogue generation to enhance the empathy expression. Extensive experiments demonstrate that our multi-perspective model outperforms the state-of-the-art models in both automatic and manual evaluation.

翻译：共情是构建更智能、人性化对话代理时需要考量的重要特性。然而现有方法未能充分理解共情作为涉及认知、情感与行为三个维度的复杂过程。本文提出CAB这一全新框架，从认知、情感与行为综合视角生成共情回应。在认知层面，我们利用外部知识构建对话中关键词语间的路径。这是因为对话中的关键词汇是句子的核心。通过建立关键词间的逻辑关系（这是多数现有工作所忽略的），能提升对关键词及上下文逻辑的理解，从而增强认知能力。在情感层面，我们采用包含对话双方情绪的潜变量对来捕捉情感依赖关系。原因在于同时考虑双方情绪有助于学习情感依赖关系。在行为层面，我们使用恰当的对话行为引导对话生成以强化共情表达。大量实验表明，我们的多视角模型在自动评估与人工评估中均优于当前最先进模型。