Covering Uncommon Ground: Gap-Focused Question Generation for Answer
  Assessment
Roni Rabin Alexandre Djerbetian Roee Engelberg Lidan Hackmon Gal Elidan Reut Tsarfaty Amir Globerson
Human communication often involves information gaps between the
interlocutors. For example, in an educational dialogue, a student often
provides an answer that is incomplete, and there is a gap between this answer
and the perfect one expected by the teacher. Successful dialogue then hinges on
the teacher asking about this gap in an effective manner, thus creating a rich
and interactive educational experience. We focus on the problem of generating
such gap-focused questions (GFQs) automatically. We define the task, highlight
key desired aspects of a good GFQ, and propose a model that satisfies these.
Finally, we provide an evaluation by human annotators of our generated
questions compared against human generated ones, demonstrating competitive
performance.