Knowledge Enhanced Reflection Generation for Counseling Dialogues

In this paper, we study the effect of commonsense and domain knowledge while generating responses in counseling conversations using retrieval and generative methods for knowledge integration. We propose a pipeline that collects domain knowledge through web mining, and show that retrieval from both domain-specific and commonsense knowledge bases improves the quality of generated responses. We also present a model that incorporates knowledge generated by COMET using soft positional encoding and masked self-attention. We show that both retrieved and COMET-generated knowledge improve the system's performance as measured by automatic metrics and by human evaluation. Lastly, we present a comparative study on the types of knowledge encoded by our system, showing that causal and intentional relationships benefit the generation task more than other types of commonsense relations.