  We study the problem of semi-supervised question answering----utilizing
unlabeled text to boost the performance of question answering models. We
propose a novel training framework, the Generative Domain-Adaptive Nets. In
this framework, we train a generative model to generate questions based on the
unlabeled text, and combine model-generated questions with human-generated
questions for training question answering models. We develop novel domain
adaptation algorithms, based on reinforcement learning, to alleviate the
discrepancy between the model-generated data distribution and the
human-generated data distribution. Experiments show that our proposed framework
obtains substantial improvement from unlabeled text.
