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.
Spoken dialogue system/ Robotics
| 氏名 | コース | 研究室 | 役職/学年 |
|---|---|---|---|
| Yahui Fu | 知能情報学コース | Speech and Audio Processing Laboratory | 博士3回生 |
| Koji Inoue | 知能情報学コース | Speech and Audio Processing Laboratory | 助教 |
| Chenhui Chu | 知能情報学コース | Language Media Processing | 特定准教授 |
| Tatsuya Kawahara | 知能情報学コース | Speech and Audio Processing Laboratory | 教授 |