  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.
