Improving Empathetic Dialogue Generation by Dynamically Infusing
  Commonsense Knowledge
Hua Cai Xuli Shen Qing Xu Weilin Shen Xiaomei Wang Weifeng Ge Xiaoqing Zheng Xiangyang Xue
In empathetic conversations, individuals express their empathy towards
others. Previous work has mainly focused on generating empathetic responses by
utilizing the speaker's emotion. Besides, external commonsense knowledge has
been applied to enhance the system's understandings of the speaker's situation.
However, given an event, commonsense knowledge base contains various relations,
potentially leading to confusion for the dialogue system. Consequently,
inconsistencies arise among the emotion, generated response and speaker's
contextual information. To this end, we propose a novel approach for empathetic
response generation, which incorporates an adaptive module for commonsense
knowledge selection to ensure consistency between the generated empathetic
responses and the speaker's situation. This selected knowledge is used to
refine the commonsense cognition and empathy expression for generated
responses. Experimental results show that our approach significantly
outperforms baseline models in both automatic and human evaluations, exhibiting
the generation of more coherent and empathetic responses. Moreover, case
studies highlight the interpretability of knowledge selection in the responses
and the effectiveness of adaptive module in our model. Code:
https://github.com/Hanscal/DCKS.