Developing conversational agents to engage in complex dialogues is challenging partly because the dialogue policy needs to explore a large state-action space. In this paper, we propose a divide-and-conquer approach that discovers and exploits the hidden structure of the task to enable efficient policy learning. First, given a set of successful dialogue sessions, we present a Subgoal Discovery Network (SDN) to divide a complex goal-oriented task into a set of simpler subgoals in an unsupervised fashion. We then use these subgoals to learn a hierarchical policy which consists of 1) a top-level policy that selects among subgoals, and 2) a low-level policy that selects primitive actions to accomplish the subgoal. We exemplify our method by building a dialogue agent for the composite task of travel planning. Experiments with simulated and real users show that an agent trained with automatically discovered subgoals performs competitively against an agent with human-defined subgoals, and significantly outperforms an agent without subgoals. Moreover, we show that learned subgoals are human comprehensible.
During the past decade, several areas of speech and language understanding have witnessed substantial breakthroughs from the use of data-driven models. In the area of dialogue systems, the trend is less obvious, and most practical systems are still built through significant engineering and expert knowledge. Nevertheless, several recent results suggest that data-driven approaches are feasible and quite promising. To facilitate research in this area, we have carried out a wide survey of publicly available datasets suitable for data-driven learning of dialogue systems. We discuss important characteristics of these datasets, how they can be used to learn diverse dialogue strategies, and their other potential uses. We also examine methods for transfer learning between datasets and the use of external knowledge. Finally, we discuss appropriate choice of evaluation metrics for the learning objective.