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This paper tackles the problem of learning a questioner in the goal-orientedvisual dialog task. Several previous works adopt model-free reinforcementlearning. Most pretrain the model from a finite set of human-generated data. Weargue that using limited demonstrations to kick-start the questioner isinsufficient due to the large policy search space. Inspired by a recentlyproposed information theoretic approach, we develop two analytic experts toserve as a source of high-quality demonstrations for imitation learning. Wethen take advantage of reinforcement learning to refine the model towards thegoal-oriented objective. Experimental results on the GuessWhat?! dataset showthat our method has the combined merits of imitation and reinforcementlearning, achieving the state-of-the-art performance.

Learning Goal-Oriented Visual Dialog Agents: Imitating and Surpassing Analytic Experts