How to generate informative, coherent and sustainable open-domain conversations is a non-trivial task. Previous work on knowledge grounded conversation generation focus on improving dialog informativeness with little attention on dialog coherence. In this paper, to enhance multi-turn dialog coherence, we propose to leverage event chains to help determine a sketch of a multi-turn dialog. We first extract event chains from narrative texts and connect them as a graph. We then present a novel event graph grounded Reinforcement Learning (RL) framework. It conducts high-level response content (simply an event) planning by learning to walk over the graph, and then produces a response conditioned on the planned content. In particular, we devise a novel multi-policy decision making mechanism to foster a coherent dialog with both appropriate content ordering and high contextual relevance. Experimental results indicate the effectiveness of this framework in terms of dialog coherence and informativeness.
Policy Gradient Reinforcement Learning with a Fuzzy Controller for Policy: Decision Making in RoboCup Soccer Small Size League