K-ESConv: Knowledge Injection for Emotional Support Dialogue Systems via
  Prompt Learning
Wei Chen Gang Zhao Xiaojin Zhang Xiang Bai Xuanjing Huang Zhongyu Wei
Automatic psychological counseling requires mass of professional knowledge
that can be found in online counseling forums. Motivated by this, we propose
K-ESConv, a novel prompt learning based knowledge injection method for
emotional support dialogue system, transferring forum knowledge to response
generation. We evaluate our model on an emotional support dataset ESConv, where
the model retrieves and incorporates knowledge from external professional
emotional Q\&A forum. Experiment results show that the proposed method
outperforms existing baselines on both automatic evaluation and human
evaluation, which shows that our approach significantly improves the
correlation and diversity of responses and provides more comfort and better
suggestion for the seeker.