Modeling question asking using neural program generation

Understand

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.