  A good dialogue agent should have the ability to interact with users by both
responding to questions and by asking questions, and importantly to learn from
both types of interaction. In this work, we explore this direction by designing
a simulator and a set of synthetic tasks in the movie domain that allow such
interactions between a learner and a teacher. We investigate how a learner can
benefit from asking questions in both offline and online reinforcement learning
settings, and demonstrate that the learner improves when asking questions.
Finally, real experiments with Mechanical Turk validate the approach. Our work
represents a first step in developing such end-to-end learned interactive
dialogue agents.
