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.
|Title of host publication||Short Papers|
|Publisher||Association for Computational Linguistics (ACL)|
|Number of pages||13|
|State||Published - 2023|
|Event||61st Annual Meeting of the Association for Computational Linguistics, ACL 2023 - Toronto, Canada|
Duration: 9 Jul 2023 → 14 Jul 2023
|Name||Proceedings of the Annual Meeting of the Association for Computational Linguistics|
|Conference||61st Annual Meeting of the Association for Computational Linguistics, ACL 2023|
|Period||9/07/23 → 14/07/23|
Bibliographical notePublisher Copyright:
© 2023 Association for Computational Linguistics.