@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",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Covering Uncommon Ground: Gap-Focused Question Generation for Answer Assessment
%A Rabin, Roni
%A Djerbetian, Alexandre
%A Engelberg, Roee
%A Hackmon, Lidan
%A Elidan, Gal
%A Tsarfaty, Reut
%A Globerson, Amir
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F rabin-etal-2023-covering
%X 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.
%R 10.18653/v1/2023.acl-short.20
%U https://aclanthology.org/2023.acl-short.20
%U https://doi.org/10.18653/v1/2023.acl-short.20
%P 215-227
Markdown (Informal)
[Covering Uncommon Ground: Gap-Focused Question Generation for Answer Assessment](https://aclanthology.org/2023.acl-short.20) (Rabin et al., ACL 2023)
ACL