@inproceedings{yuan-etal-2017-machine,
title = "Machine Comprehension by Text-to-Text Neural Question Generation",
author = "Yuan, Xingdi and
Wang, Tong and
Gulcehre, Caglar and
Sordoni, Alessandro and
Bachman, Philip and
Zhang, Saizheng and
Subramanian, Sandeep and
Trischler, Adam",
editor = "Blunsom, Phil and
Bordes, Antoine and
Cho, Kyunghyun and
Cohen, Shay and
Dyer, Chris and
Grefenstette, Edward and
Hermann, Karl Moritz and
Rimell, Laura and
Weston, Jason and
Yih, Scott",
booktitle = "Proceedings of the 2nd Workshop on Representation Learning for {NLP}",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-2603",
doi = "10.18653/v1/W17-2603",
pages = "15--25",
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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%0 Conference Proceedings
%T Machine Comprehension by Text-to-Text Neural Question Generation
%A Yuan, Xingdi
%A Wang, Tong
%A Gulcehre, Caglar
%A Sordoni, Alessandro
%A Bachman, Philip
%A Zhang, Saizheng
%A Subramanian, Sandeep
%A Trischler, Adam
%Y Blunsom, Phil
%Y Bordes, Antoine
%Y Cho, Kyunghyun
%Y Cohen, Shay
%Y Dyer, Chris
%Y Grefenstette, Edward
%Y Hermann, Karl Moritz
%Y Rimell, Laura
%Y Weston, Jason
%Y Yih, Scott
%S Proceedings of the 2nd Workshop on Representation Learning for NLP
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F yuan-etal-2017-machine
%X 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.
%R 10.18653/v1/W17-2603
%U https://aclanthology.org/W17-2603
%U https://doi.org/10.18653/v1/W17-2603
%P 15-25
Markdown (Informal)
[Machine Comprehension by Text-to-Text Neural Question Generation](https://aclanthology.org/W17-2603) (Yuan et al., RepL4NLP 2017)
ACL
- Xingdi Yuan, Tong Wang, Caglar Gulcehre, Alessandro Sordoni, Philip Bachman, Saizheng Zhang, Sandeep Subramanian, and Adam Trischler. 2017. Machine Comprehension by Text-to-Text Neural Question Generation. In Proceedings of the 2nd Workshop on Representation Learning for NLP, pages 15–25, Vancouver, Canada. Association for Computational Linguistics.