  A proactive dialogue system has the ability to proactively lead the
conversation. Different from the general chatbots which only react to the user,
proactive dialogue systems can be used to achieve some goals, e.g., to
recommend some items to the user. Background knowledge is essential to enable
smooth and natural transitions in dialogue. In this paper, we propose a new
multi-task learning framework for retrieval-based knowledge-grounded proactive
dialogue. To determine the relevant knowledge to be used, we frame knowledge
prediction as a complementary task and use explicit signals to supervise its
learning. The final response is selected according to the predicted knowledge,
the goal to achieve, and the context. Experimental results show that explicit
modeling of knowledge prediction and goal selection can greatly improve the
final response selection. Our code is available at
https://github.com/DaoD/KPN/.
