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Recent approaches to empathetic response generation try to incorporatecommonsense knowledge or reasoning about the causes of emotions to betterunderstand the user's experiences and feelings. However, these approachesmainly focus on understanding the causalities of context from the user'sperspective, ignoring the system's perspective. In this paper, we propose acommonsense-based causality explanation approach for diverse empatheticresponse generation that considers both the user's perspective (user's desiresand reactions) and the system's perspective (system's intentions andreactions). We enhance ChatGPT's ability to reason for the system's perspectiveby integrating in-context learning with commonsense knowledge. Then, weintegrate the commonsense-based causality explanation with both ChatGPT and aT5-based model. Experimental evaluations demonstrate that our methodoutperforms other comparable methods on both automatic and human evaluations.

Reasoning before Responding: Integrating Commonsense-based Causality Explanation for Empathetic Response Generation