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、人性化对话智能体时需考虑的重要特征。然而现有方法未能完全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框架——一种综合认知、情感与行为视角生成共情回应的新颖方案。在认知层面，我们通过利用外部知识构建对话中关键词语之间的路径。这是因为对话中的关键词是句子核心，而现存多数工作忽略的关键词语义逻辑关系构建，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，我们采用包含双方说话者情感的双重潜变量捕获情感依赖关系，其原因在于同时考虑对话双方情感有助于学习情感依赖。在行为层面，我们运用适宜的对话行为引导对话生成以强化共情表达。大量实验证明，我们的多视角模型在自动评估与人工评估中均优于现有最优模型。