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.
Original language | English |
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Title of host publication | Short Papers |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 215-227 |
Number of pages | 13 |
ISBN (Electronic) | 9781959429715 |
State | Published - 2023 |
Event | 61st Annual Meeting of the Association for Computational Linguistics, ACL 2023 - Toronto, Canada Duration: 9 Jul 2023 → 14 Jul 2023 |
Publication series
Name | Proceedings of the Annual Meeting of the Association for Computational Linguistics |
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Volume | 2 |
ISSN (Print) | 0736-587X |
Conference
Conference | 61st Annual Meeting of the Association for Computational Linguistics, ACL 2023 |
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Country/Territory | Canada |
City | Toronto |
Period | 9/07/23 → 14/07/23 |
Bibliographical note
Publisher Copyright:© 2023 Association for Computational Linguistics.