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.

该研究提出了 CAB 框架，该框架从认知、情感和行为三个角度出发，生成具有同情心的响应，并在自动和手动评估中均打败了现有的最先进模型。