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We propose a recurrent neural model that generates natural-language questionsfrom documents, conditioned on answers. We show how to train the model using acombination of supervised and reinforcement learning. After teacher forcing forstandard maximum likelihood training, we fine-tune the model using policygradient techniques to maximize several rewards that measure question quality.Most notably, one of these rewards is the performance of a question-answeringsystem. We motivate question generation as a means to improve the performanceof question answering systems. Our model is trained and evaluated on the recentquestion-answering dataset SQuAD.

Machine Comprehension by Text-to-Text Neural Question Generation