PLATO-KAG: Unsupervised Knowledge-Grounded Conversation via Joint Modeling
01 Nov 2021-pp 143-154
TL;DR: This paper propose an unsupervised learning approach for end-to-end knowledge-grounded conversation modeling, where the top-k relevant knowledge elements are selected and then employed in knowledge-based response generation.
Abstract: Large-scale conversation models are turning to leveraging external knowledge to improve the factual accuracy in response generation. Considering the infeasibility to annotate the external knowledge for large-scale dialogue corpora, it is desirable to learn the knowledge selection and response generation in an unsupervised manner. In this paper, we propose PLATO-KAG (Knowledge-Augmented Generation), an unsupervised learning approach for end-to-end knowledge-grounded conversation modeling. For each dialogue context, the top-k relevant knowledge elements are selected and then employed in knowledge-grounded response generation. The two components of knowledge selection and response generation are optimized jointly and effectively under a balanced objective. Experimental results on two publicly available datasets validate the superiority of PLATO-KAG.
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