Covering Uncommon Ground: Gap-Focused Question Generation for Answer Assessment
Roni Rabin, Alexandre Djerbetian, Roee Engelberg, Lidan Hackmon, Gal Elidan, Reut Tsarfaty, Amir Globerson
Abstract
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.
BibTeX
@inproceedings{rabin-etal-2023-covering,
title = "Covering Uncommon Ground: Gap-Focused Question Generation for Answer Assessment",
author = "Rabin, Roni and
Djerbetian, Alexandre and
Engelberg, Roee and
Hackmon, Lidan and
Elidan, Gal and
Tsarfaty, Reut and
Globerson, Amir",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-short.20/",
doi = "10.18653/v1/2023.acl-short.20",
pages = "215--227"
}