Commonsense-Aware Prompting for Controllable Empathetic Dialogue Generation
This work addresses emotional awareness in dialogue systems for applications like chatbots, but it is incremental as it builds on prior methods.
The paper tackled the problem of improving empathetic dialogue generation by incorporating commonsense knowledge and controlling generation, resulting in enhanced performance as shown in experiments.
Improving the emotional awareness of pre-trained language models is an emerging important problem for dialogue generation tasks. Although prior studies have introduced methods to improve empathetic dialogue generation, few have discussed how to incorporate commonsense knowledge into pre-trained language models for controllable dialogue generation. In this study, we propose a novel framework that improves empathetic dialogue generation using pre-trained language models by 1) incorporating commonsense knowledge through prompt verbalization, and 2) controlling dialogue generation using a strategy-driven future discriminator. We conducted experiments to reveal that both the incorporation of social commonsense knowledge and enforcement of control over generation help to improve generation performance. Finally, we discuss the implications of our study for future research.