  People ask questions that are far richer, more informative, and more creative
than current AI systems. We propose a neuro-symbolic framework for modeling
human question asking, which represents questions as formal programs and
generates programs with an encoder-decoder based deep neural network. From
extensive experiments using an information-search game, we show that our method
can predict which questions humans are likely to ask in unconstrained settings.
We also propose a novel grammar-based question generation framework trained
with reinforcement learning, which is able to generate creative questions
without supervised human data.
