Machine Comprehension by Text-to-Text Neural Question Generation
Xingdi Yuan, Tong Wang, Caglar Gulcehre, Alessandro Sordoni, Philip Bachman, Saizheng Zhang, Sandeep Subramanian, Adam Trischler
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.- Anthology ID:
- W17-2603
- Volume:
- Proceedings of the 2nd Workshop on Representation Learning for NLP
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada
- Venue:
- RepL4NLP
- SIG:
- SIGREP
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 15–25
- Language:
- URL:
- https://aclanthology.org/W17-2603
- DOI:
- 10.18653/v1/W17-2603
- Cite (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.
- Cite (Informal):
- Machine Comprehension by Text-to-Text Neural Question Generation (Yuan et al., RepL4NLP 2017)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/W17-2603.pdf
- Code
- additional community code
- Data
- SQuAD