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In open-domain conversational systems, it is important but challenging toleverage background knowledge. We can use the incorporation of knowledge tomake the generation of dialogue controllable, and can generate more diversesentences that contain real knowledge. In this paper, we combine the knowledgebases and pre-training model to propose a knowledge-driven conversation system.The system includes modules such as dialogue topic prediction, knowledgematching and dialogue generation. Based on this system, we study theperformance factors that maybe affect the generation of knowledge-drivendialogue: topic coarse recall algorithm, number of knowledge choices,generation model choices, etc., and finally made the system reachstate-of-the-art. These experimental results will provide some guidingsignificance for the future research of this task. As far as we know, this isthe first work to study and analyze the effects of the related factors.

Prediction, Selection, and Generation: Exploration of Knowledge-Driven Conversation System