@inproceedings{hadifar-etal-2023-diverse,
title = "Diverse Content Selection for Educational Question Generation",
author = "Hadifar, Amir and
Bitew, Semere Kiros and
Deleu, Johannes and
Hoste, Veronique and
Develder, Chris and
Demeester, Thomas",
editor = "Bassignana, Elisa and
Lindemann, Matthias and
Petit, Alban",
booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.eacl-srw.13",
doi = "10.18653/v1/2023.eacl-srw.13",
pages = "123--133",
abstract = "Question Generation (QG) systems have shown promising results in reducing the time and effort required to create questions for students. Typically, a first step in QG is to select the content to design a question for. In an educational setting, it is crucial that the resulting questions cover the most relevant/important pieces of knowledge the student should have acquired. Yet, current QG systems either consider just a single sentence or paragraph (thus do not include a selection step), or do not consider this educational viewpoint of content selection. Aiming to fill this research gap with a solution for educational document level QG, we thus propose to select contents for QG based on relevance and topic diversity. We demonstrate the effectiveness of our proposed content selection strategy for QG on 2 educational datasets. In our performance assessment, we also highlight limitations of existing QG evaluation metrics in light of the content selection problem.",
}
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<abstract>Question Generation (QG) systems have shown promising results in reducing the time and effort required to create questions for students. Typically, a first step in QG is to select the content to design a question for. In an educational setting, it is crucial that the resulting questions cover the most relevant/important pieces of knowledge the student should have acquired. Yet, current QG systems either consider just a single sentence or paragraph (thus do not include a selection step), or do not consider this educational viewpoint of content selection. Aiming to fill this research gap with a solution for educational document level QG, we thus propose to select contents for QG based on relevance and topic diversity. We demonstrate the effectiveness of our proposed content selection strategy for QG on 2 educational datasets. In our performance assessment, we also highlight limitations of existing QG evaluation metrics in light of the content selection problem.</abstract>
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%0 Conference Proceedings
%T Diverse Content Selection for Educational Question Generation
%A Hadifar, Amir
%A Bitew, Semere Kiros
%A Deleu, Johannes
%A Hoste, Veronique
%A Develder, Chris
%A Demeester, Thomas
%Y Bassignana, Elisa
%Y Lindemann, Matthias
%Y Petit, Alban
%S Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F hadifar-etal-2023-diverse
%X Question Generation (QG) systems have shown promising results in reducing the time and effort required to create questions for students. Typically, a first step in QG is to select the content to design a question for. In an educational setting, it is crucial that the resulting questions cover the most relevant/important pieces of knowledge the student should have acquired. Yet, current QG systems either consider just a single sentence or paragraph (thus do not include a selection step), or do not consider this educational viewpoint of content selection. Aiming to fill this research gap with a solution for educational document level QG, we thus propose to select contents for QG based on relevance and topic diversity. We demonstrate the effectiveness of our proposed content selection strategy for QG on 2 educational datasets. In our performance assessment, we also highlight limitations of existing QG evaluation metrics in light of the content selection problem.
%R 10.18653/v1/2023.eacl-srw.13
%U https://aclanthology.org/2023.eacl-srw.13
%U https://doi.org/10.18653/v1/2023.eacl-srw.13
%P 123-133
Markdown (Informal)
[Diverse Content Selection for Educational Question Generation](https://aclanthology.org/2023.eacl-srw.13) (Hadifar et al., EACL 2023)
ACL
- Amir Hadifar, Semere Kiros Bitew, Johannes Deleu, Veronique Hoste, Chris Develder, and Thomas Demeester. 2023. Diverse Content Selection for Educational Question Generation. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop, pages 123–133, Dubrovnik, Croatia. Association for Computational Linguistics.