  Text-based Question Generation (QG) aims at generating natural and relevant
questions that can be answered by a given answer in some context. Existing QG
models suffer from a "semantic drift" problem, i.e., the semantics of the
model-generated question drifts away from the given context and answer. In this
paper, we first propose two semantics-enhanced rewards obtained from downstream
question paraphrasing and question answering tasks to regularize the QG model
to generate semantically valid questions. Second, since the traditional
evaluation metrics (e.g., BLEU) often fall short in evaluating the quality of
generated questions, we propose a QA-based evaluation method which measures the
QG model's ability to mimic human annotators in generating QA training data.
Experiments show that our method achieves the new state-of-the-art performance
w.r.t. traditional metrics, and also performs best on our QA-based evaluation
metrics. Further, we investigate how to use our QG model to augment QA datasets
and enable semi-supervised QA. We propose two ways to generate synthetic QA
pairs: generate new questions from existing articles or collect QA pairs from
new articles. We also propose two empirically effective strategies, a data
filter and mixing mini-batch training, to properly use the QG-generated data
for QA. Experiments show that our method improves over both BiDAF and BERT QA
baselines, even without introducing new articles.
