  Existing question answering (QA) techniques are created mainly to answer
questions asked by humans. But in educational applications, teachers often need
to decide what questions they should ask, in order to help students to improve
their narrative understanding capabilities. We design an automated
question-answer generation (QAG) system for this education scenario: given a
story book at the kindergarten to eighth-grade level as input, our system can
automatically generate QA pairs that are capable of testing a variety of
dimensions of a student's comprehension skills. Our proposed QAG model
architecture is demonstrated using a new expert-annotated FairytaleQA dataset,
which has 278 child-friendly storybooks with 10,580 QA pairs. Automatic and
human evaluations show that our model outperforms state-of-the-art QAG baseline
systems. On top of our QAG system, we also start to build an interactive
story-telling application for the future real-world deployment in this
educational scenario.
