Reasoning before Responding: Integrating Commonsense-based Causality Explanation for Empathetic Response Generation
This work addresses the need for more nuanced empathetic AI responses in conversational systems, though it is incremental by building on existing commonsense reasoning approaches.
The paper tackles the problem of empathetic response generation by integrating commonsense-based causality explanations from both user and system perspectives, resulting in improved performance over comparable methods in automatic and human evaluations.
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.