  Neural network-based methods represent the state-of-the-art in question
generation from text. Existing work focuses on generating only questions from
text without concerning itself with answer generation. Moreover, our analysis
shows that handling rare words and generating the most appropriate question
given a candidate answer are still challenges facing existing approaches. We
present a novel two-stage process to generate question-answer pairs from the
text. For the first stage, we present alternatives for encoding the span of the
pivotal answer in the sentence using Pointer Networks. In our second stage, we
employ sequence to sequence models for question generation, enhanced with rich
linguistic features. Finally, global attention and answer encoding are used for
generating the question most relevant to the answer. We motivate and
linguistically analyze the role of each component in our framework and consider
compositions of these. This analysis is supported by extensive experimental
evaluations. Using standard evaluation metrics as well as human evaluations,
our experimental results validate the significant improvement in the quality of
questions generated by our framework over the state-of-the-art. The technique
presented here represents another step towards more automated reading
comprehension assessment. We also present a live system \footnote{Demo of the
system is available at
\url{https://www.cse.iitb.ac.in/~vishwajeet/autoqg.html}.} to demonstrate the
effectiveness of our approach.
