Machine Comprehension by Text-to-Text Neural Question Generation - Yuan, Xingdi; Wang, Tong; Gulcehre, Caglar; Sordoni, Alessandro; Bachman, Philip; Subramanian, Sandeep; Zhang, Saizheng; Trischler, Adam (RepL4NLP workshop, ACL 2017, 2017)
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.