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We investigate the problem of generating informative questions ininformation-asymmetric conversations. Unlike previous work on questiongeneration which largely assumes knowledge of what the answer might be, we areinterested in the scenario where the questioner is not given the context fromwhich answers are drawn, but must reason pragmatically about how to acquire newinformation, given the shared conversation history. We identify two corechallenges: (1) formally defining the informativeness of potential questions,and (2) exploring the prohibitively large space of potential questions to findthe good candidates. To generate pragmatic questions, we use reinforcementlearning to optimize an informativeness metric we propose, combined with areward function designed to promote more specific questions. We demonstratethat the resulting pragmatic questioner substantially improves theinformativeness and specificity of questions generated over a baseline model,as evaluated by our metrics as well as humans.

Stay Hungry, Stay Focused: Generating Informative and Specific Questions in Information-Seeking Conversations