Covering Uncommon Ground: Followup Question Generation for Answer Assessment
Abstract
In educational dialogue settings students often provide answers that are incomplete. In other words, there is a gap between the answer the student provides and the perfect answer expected by the teacher. Successful dialogue hinges on the teacher asking about this gap in an effective manner, thus creating a rich and interactive educational experience. Here we focus on the problem of generating such gap-focused questions (GFQ) 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 of our generated questions and compare them to manually generated ones, demonstrating competitive performance.
Research Areas
