Covering Uncommon Ground: Gap-Focused Question Generation for Answer Assessment
This work addresses the need for interactive educational tools by automating question generation, though it is incremental as it builds on existing dialogue and question generation methods.
The paper tackled the problem of automatically generating gap-focused questions to address information gaps in educational dialogues, achieving competitive performance compared to human-generated questions.
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.