ISCA Archive Interspeech 2024
ISCA Archive Interspeech 2024

Applying Reinforcement Learning and Multi-Generators for Stage Transition in an Emotional Support Dialogue System

Jeremy Chang, Kuan-Yu Chen, Chung-Hsien Wu

The use of empathetic dialogue systems has grown recently. However, establishing them for users experiencing mental depression requires more advanced consoling skills. In this paper, a dialogue system based on Emotional Support was developed. The system offers coping strategies through stages designed to address users' distress in long-term conversations. It employs a recurrent-based approach integrated with reinforcement learning for a decision model, which selects a generator from three specialized conditional generation models to generate empathetic responses. Experimental results showed improvements in BLEU, Rouge-L, and Distinct-n metrics compared to the baseline. On average, the system's BLEU score increased by 0.87, Rouge-L by 1.85, Distinct-1 by 0.69, and Distinct-2 by 2.26. As a result, the system generates responses aligned with Emotional Support skills, ultimately comforting the user’s distress.