Prediction, Selection, and Generation: Exploration of Knowledge-Driven
  Conversation System
  In open-domain conversational systems, it is important but challenging to
leverage background knowledge. We can use the incorporation of knowledge to
make the generation of dialogue controllable, and can generate more diverse
sentences that contain real knowledge. In this paper, we combine the knowledge
bases and pre-training model to propose a knowledge-driven conversation system.
The system includes modules such as dialogue topic prediction, knowledge
matching and dialogue generation. Based on this system, we study the
performance factors that maybe affect the generation of knowledge-driven
dialogue: topic coarse recall algorithm, number of knowledge choices,
generation model choices, etc., and finally made the system reach
state-of-the-art. These experimental results will provide some guiding
significance for the future research of this task. As far as we know, this is
the first work to study and analyze the effects of the related factors.
