Recent approaches to empathetic response generation try to incorporate commonsense knowledge or reasoning about the causes of emotions to better understand the user's experiences and feelings. However, these approaches mainly focus on understanding the causalities of context from the user's perspective, ignoring the system's perspective. In this paper, we propose a commonsense-based causality explanation approach for diverse empathetic response generation that considers both the user's perspective (user's desires and reactions) and the system's perspective (system's intentions and reactions). We enhance ChatGPT's ability to reason for the system's perspective by integrating in-context learning with commonsense knowledge. Then, we integrate the commonsense-based causality explanation with both ChatGPT and a T5-based model. Experimental evaluations demonstrate that our method outperforms other comparable methods on both automatic and human evaluations.

翻译：近期共情回复生成方法尝试融入常识知识或对情绪原因的推理，以更好地理解用户的经历和感受。然而，这些方法主要聚焦从用户视角理解上下文的因果关系，忽略了系统视角。本文提出一种基于常识的因果解释方法，用于生成多样化共情回复，同时考虑用户视角（用户的愿望与反应）和系统视角（系统的意图与反应）。我们通过将上下文学习与常识知识融合，增强ChatGPT从系统视角进行推理的能力。随后，我们将基于常识的因果解释同时融入ChatGPT和基于T5的模型。实验评估表明，我们的方法在自动评估和人工评估上均优于其他可比方法。