# Modeling question asking using neural program generation

arXiv: https://arxiv.org/abs/1907.09899 · HTML: https://mlsanity.com/paper/1907.09899/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2019.

## Built on by (most cited first, 1 of 1)

- [Open-domain clarification question generation without question examples](https://mlsanity.com/paper/2110.09779/) (2021)

## Abstract (arXiv)

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.