Machine Comprehension by Text-to-Text Neural Question Generation

Xingdi Yuan; Tong Wang; Caglar Gulcehre; Alessandro Sordoni; Philip Bachman; Sandeep Subramanian; Saizheng Zhang; Adam Trischler

arXiv · 1705.02012

Machine Comprehension by Text-to-Text Neural Question Generation

Xingdi Yuan·Tong Wang·Caglar Gulcehre·Alessandro Sordoni·Philip Bachman·Sandeep Subramanian·Saizheng Zhang·Adam Trischler

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Abstract

We propose a recurrent neural model that generates natural-language questions from documents, conditioned on answers. We show how to train the model using a combination of supervised and reinforcement learning. After teacher forcing for standard maximum likelihood training, we fine-tune the model using policy gradient techniques to maximize several rewards that measure question quality. Most notably, one of these rewards is the performance of a question-answering system. We motivate question generation as a means to improve the performance of question answering systems. Our model is trained and evaluated on the recent question-answering dataset SQuAD.

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Xingdi Yuan, Tong Wang, Caglar Gulcehre, Alessandro Sordoni, Philip Bachman, Sandeep Subramanian, Saizheng Zhang, Adam Trischler. 2017-05-04. Machine Comprehension by Text-to-Text Neural Question Generation. https://arxiv.org/abs/1705.02012

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