A Target-Driven Planning Approach for Goal-Directed Dialog Systems

Existing dialog systems mainly build social bonds reactively with users for chitchat or assist users with specific tasks. In this work, we push forward to a promising yet under-explored proactive dialog paradigm called goal-directed dialog systems, where the “goal” refers to achieving the recommendation for a predetermined target topic through social conversations. We focus on how to make plans that naturally lead users to achieve the goal through smooth topic transitions. To this end, we propose a target-driven planning network (TPNet) to drive the system to transit between different conversation stages. Built upon the widely used transformer architecture, TPNet frames the complicated planning process as a sequence generation task, which plans a dialog path consisting of dialog actions and topics. We then apply our TPNet with planned content to guide dialog generation using various backbone models. Extensive experiments show that our approach obtains the state-of-the-art performance in automatic and human evaluations. The results demonstrate that TPNet affects the improvement of goal-directed dialog systems significantly.