  In this work, we focus on the task of Automatic Question Generation (AQG)
where given a passage and an answer the task is to generate the corresponding
question. It is desired that the generated question should be (i) grammatically
correct (ii) answerable from the passage and (iii) specific to the given
answer. An analysis of existing AQG models shows that they produce questions
which do not adhere to one or more of {the above-mentioned qualities}. In
particular, the generated questions look like an incomplete draft of the
desired question with a clear scope for refinement. {To alleviate this
shortcoming}, we propose a method which tries to mimic the human process of
generating questions by first creating an initial draft and then refining it.
More specifically, we propose Refine Network (RefNet) which contains two
decoders. The second decoder uses a dual attention network which pays attention
to both (i) the original passage and (ii) the question (initial draft)
generated by the first decoder. In effect, it refines the question generated by
the first decoder, thereby making it more correct and complete. We evaluate
RefNet on three datasets, \textit{viz.}, SQuAD, HOTPOT-QA, and DROP, and show
that it outperforms existing state-of-the-art methods by 7-16\% on all of these
datasets. Lastly, we show that we can improve the quality of the second decoder
on specific metrics, such as, fluency and answerability by explicitly rewarding
revisions that improve on the corresponding metric during training. The code
has been made publicly available
\footnote{https://github.com/PrekshaNema25/RefNet-QG}
