Automatic question generation aims to generate questions from a text passage where the generated questions can be answered by certain sub-spans of the given passage. Traditional methods mainly use rigid heuristic rules to transform a sentence into related questions. In this work, we propose to apply the neural encoder-decoder model to generate meaningful and diverse questions from natural language sentences. The encoder reads the input text and the answer position, to produce an answer-aware input representation, which is fed to the decoder to generate an answer focused question. We conduct a preliminary study on neural question generation from text with the SQuAD dataset, and the experiment results show that our method can produce fluent and diverse questions.
@article{arxiv.1704.01792,
title = {Neural Question Generation from Text: A Preliminary Study},
author = {Qingyu Zhou and Nan Yang and Furu Wei and Chuanqi Tan and Hangbo Bao and Ming Zhou},
journal= {arXiv preprint arXiv:1704.01792},
year = {2017}
}