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. Our model is trained and evaluated on the recent question-answering dataset SQuAD.
Another recent work is by REF , they generate questions from documents using supervised and reinforcement learning.
Machine Comprehension by Text-to-Text Neural Question Generation
ArXiv
ArXiv
Computer Science