Empathy is a trait that naturally manifests in human conversation.
Theoretically, the birth of empathetic responses results from conscious
alignment and interaction between cognition and affection of empathy. However,
existing works rely solely on a single affective aspect or model cognition and
affection independently, limiting the empathetic capabilities of the generated
responses. To this end, based on the commonsense cognition graph and emotional
concept graph constructed involving commonsense and concept knowledge, we
design a two-level strategy to align coarse-grained (between contextual
cognition and contextual emotional state) and fine-grained (between each
specific cognition and corresponding emotional reaction) Cognition and
Affection for reSponding Empathetically (CASE). Extensive experiments
demonstrate that CASE outperforms the state-of-the-art baselines on automatic
and human evaluation. Our code will be released.

使用常识认知图和情感概念图构建一个两级策略 (CASE)，以对齐粗粒度 (上下文认知和情感状态之间) 和细粒度 (每个特定认知和相应情感反应之间) 的认知和情感，从而实现共情回应，实验证明 CASE 在自动和人工评估中优于现有基线。