Improving Knowledge-Aware Dialogue Response Generation by Using Human-Written Prototype Dialogues
Sixing Wu, Ying Li, Dawei Zhang, Zhonghai Wu · 2020
Incorporating commonsense knowledge can alleviate the issue of generating generic responses in open-domain generative dialogue systems.However, selecting knowledge facts for the dialogue context is still a challenge.The widely used approach Entity Name Matching always retrieves irrelevant facts from the view of local entity words.This paper proposes a novel knowledge selection approach, Prototype-KR, and a knowledge-aware generative model, Prototype-KRG.Given a query, our approach first retrieves a set of prototype dialogues that are relevant to the query.We find knowledge facts used in prototype dialogues usually are highly relevant to the current query; thus, Prototype-KR ranks such knowledge facts based on the semantic similarity and then selects the most appropriate facts.Subsequently, Prototype-KRG can generate an informative response using the selected knowledge facts.Experiments demonstrate that our approach has achieved notable improvements on the most metrics, compared to generative baselines.Meanwhile, compared to IR(Retrieval)-based baselines, responses generated by our approach are more relevant to the context and have comparable informativeness.