CASE : Aligning Coarse-to-Fine Cognition and Affection for Empathetic Response Generation (2023)

Abstract: Empathetic conversation is psychologically supposed to be the result of conscious alignment and interaction between the cognition and affection of empathy. However, existing empathetic dialogue models usually consider only the affective aspect or treat cognition and affection in isolation, which limits the capability of empathetic response generation. In this work, we propose the CASE model for empathetic dialogue generation. It first builds upon a commonsense cognition graph and an emotional concept graph and then aligns the user’s cognition and affection at both the coarse-grained and fine-grained levels. Through automatic and manual evaluation, we demonstrate that CASE outperforms state-of-the-art baselines of empathetic dialogues and can generate more empathetic and informative responses.

## introduction human empathetic conversations allow both parties to understand each other's experiences and feelings @xcite , which is crucial for establishing seamless relationships @xcite and is also integral to building a trustful conversational ai @xcite . in social psychology, empathy consists of two aspects: cognition and affection @xcite . the cognitive aspect corresponding to the understanding of the user's situation and experiences @xcite . the affective aspect requires the comprehension of the user's emotional state and his/her potential emotional reaction @xcite . although existing work of empathetic dialogue involves both aspects of empathy, there are still issues that need to be addressed. first, most work @xcite @xcite context-1a: my family and i are going on vacation in a few weeks. we rented a largo beachfront condo and i can not wait! context-1b: we went to sunbathe by the sea about a week ago and it rained several days while we were there. to go to the beach et @xcite @xcite considers only the affective aspect, like detecting the user's emotional state to enhance empathy expression. second, although recent work explored both roles of cognition and affection in empathy expression @xcite , they usually treat cognition and affection in isolation without considering their relationship. however, human empathetic responses often result from conscious alignment and interaction between cognition and affection of empathy @xcite . for one thing, the user's overall emotional state manifested in the context suggests the user's attitude toward current situation (i.e., cognition). thus, for the listener, aligning the user's expressed cognition to the proper emotional state is essential for an appropriate empathetic response. as in case-1 of figure 1 , the alignment of cognition (i.e., intent "to go to the beach") with different emo-tional states (i.e., "excited" vs. "disappointed") produces different appropriate empathetic expressions (i.e., "love" and "which beach are you going to go to" vs. "hate" and "waiting for the beach"), respectively. for another, the user's situation drives the listener to infer the deeper specific cognitions and associate them with the underlying emotional reactions. in this way, the listener can produce a more actively empathetic response instead of only understanding and repeating the user's expressed cognition. as in case-2 of figure 1 , building association between inferred cognitions and emotional reactions, i.e., "to give up" and "frustrated" vs. "to try harder" and "hopeful", yields cognitively distinct but highly empathetic responses, i.e., response-2a vs. response-2b. the two cases highlight the necessity of aligning cognition and affection on both overall and specific (i.e., coarse and fine-grained) level for empathy modeling in response generation. to this end, we align cognition and affection for responding empathetically (case) on coarse and fine-grained levels by fusing sentence-level commonsense knowledge from comet @xcite and word-level concept knowledge from conceptnet @xcite . commonsense knowledge infers the user's situation as cognition and infers emotional reactions to the situation, which are implied in the dialogue. concept knowledge serves to extract the emotional state manifested in the dialogue. for encoding the two types of knowledge, we first construct commonsense cognition graph and emotional concept graph, where the initial independent representation of cognition and emotional concept is carefully adjusted by dialogue context adopting graph transformers. then, we design a two-level strategy to align cognition and affection using mutual information maximization (mim) (appendix a) @xcite . the coarse-grained level considers overall cognition and affection manifested in the dialogue context to align contextual cognition and contextual emotional state, which are extracted with a knowledge discernment mechanism. the fine-grained level builds the fine-grained association between cognition and affection implied in the dialogue to align each specific cognition and corresponding emotional reaction. further, an empathy-aware decoder is devised for generating empathetic expressions. our contributions are summarized as follows: ## conclusion and future work in this paper, for responding empathetically, we propose case to align cognition and affection by simulating their conscious interaction in human conversation. extensive experiments verify the superiority of case on overall quality and empathy performance. our work will also encourage future work to model the more complex interaction between cognition and affection in human conversation as well as other human language behaviors @xcite .