Covering Uncommon Ground: Gap-Focused Question Generation for Answer Assessment

by   Roni Rabin, et al.

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.


The BEA 2023 Shared Task on Generating AI Teacher Responses in Educational Dialogues

This paper describes the results of the first shared task on the generat...

The ADAIO System at the BEA-2023 Shared Task on Generating AI Teacher Responses in Educational Dialogues

This paper presents the ADAIO team's system entry in the Building Educat...

LatEval: An Interactive LLMs Evaluation Benchmark with Incomplete Information from Lateral Thinking Puzzles

With the continuous evolution and refinement of LLMs, they are endowed w...

Towards Enriched Controllability for Educational Question Generation

Question Generation (QG) is a task within Natural Language Processing (N...

Teacher-Student Framework Enhanced Multi-domain Dialogue Generation

Dialogue systems dealing with multi-domain tasks are highly required. Ho...

Computationally Identifying Funneling and Focusing Questions in Classroom Discourse

Responsive teaching is a highly effective strategy that promotes student...

Please sign up or login with your details

Forgot password? Click here to reset