Precise Knowledge Enhancement via CBR Framework for Empathetic Dialogue Generation

Empathetic dialogue systems are designed to capture emotions in conversations and provide appropriate emotional responses. Previous researches have indicated that integrating specific knowledge into empathetic dialogue systems can enhance the overall effectiveness of generating empathetic responses. Nevertheless, existing methods for knowledge-enhanced empathetic dialogue generation lack a focus on the precise selection of knowledge enhancement configurations for this specific task. To address this, we propose a Case-Based Reasoning (CBR) framework called CBR-KNOWLEDGE for autonomously select precise knowledge enhancement configurations tailored to specific empathetic dialogue contexts. Firstly, CBR-KNOWLEDGE establishes a case base that mirrors the overall quality of empathetic dialogues generated under various knowledge enhancement configurations. Subsequently, CBR-KNOWLEDGE employs an innovative text representation method, integrating an additional representation for words with noteworthy emotional impact. This approach facilitates the retrieval of analogous empathetic dialogues, enabling the reuse of their knowledge enhancement configurations to determine a new knowledge enhancement configuration. Ultimately, CBR-KNOWLEDGE employs this precise knowledge enhancement configuration for the purpose of empathetic dialogue generation. Experimental results demonstrate that CBR-KNOWLEDGE effectively enhances the performance of empathetic dialogue generation task.