@inproceedings{kalpakchi-boye-2021-bert,
title = "{BERT}-based distractor generation for {S}wedish reading comprehension questions using a small-scale dataset",
author = "Kalpakchi, Dmytro and
Boye, Johan",
editor = "Belz, Anya and
Fan, Angela and
Reiter, Ehud and
Sripada, Yaji",
booktitle = "Proceedings of the 14th International Conference on Natural Language Generation",
month = aug,
year = "2021",
address = "Aberdeen, Scotland, UK",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.inlg-1.43",
doi = "10.18653/v1/2021.inlg-1.43",
pages = "387--403",
abstract = "An important part when constructing multiple-choice questions (MCQs) for reading comprehension assessment are the distractors, the incorrect but preferably plausible answer options. In this paper, we present a new BERT-based method for automatically generating distractors using only a small-scale dataset. We also release a new such dataset of Swedish MCQs (used for training the model), and propose a methodology for assessing the generated distractors. Evaluation shows that from a student{'}s perspective, our method generated one or more plausible distractors for more than 50{\%} of the MCQs in our test set. From a teacher{'}s perspective, about 50{\%} of the generated distractors were deemed appropriate. We also do a thorough analysis of the results.",
}
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%0 Conference Proceedings
%T BERT-based distractor generation for Swedish reading comprehension questions using a small-scale dataset
%A Kalpakchi, Dmytro
%A Boye, Johan
%Y Belz, Anya
%Y Fan, Angela
%Y Reiter, Ehud
%Y Sripada, Yaji
%S Proceedings of the 14th International Conference on Natural Language Generation
%D 2021
%8 August
%I Association for Computational Linguistics
%C Aberdeen, Scotland, UK
%F kalpakchi-boye-2021-bert
%X An important part when constructing multiple-choice questions (MCQs) for reading comprehension assessment are the distractors, the incorrect but preferably plausible answer options. In this paper, we present a new BERT-based method for automatically generating distractors using only a small-scale dataset. We also release a new such dataset of Swedish MCQs (used for training the model), and propose a methodology for assessing the generated distractors. Evaluation shows that from a student’s perspective, our method generated one or more plausible distractors for more than 50% of the MCQs in our test set. From a teacher’s perspective, about 50% of the generated distractors were deemed appropriate. We also do a thorough analysis of the results.
%R 10.18653/v1/2021.inlg-1.43
%U https://aclanthology.org/2021.inlg-1.43
%U https://doi.org/10.18653/v1/2021.inlg-1.43
%P 387-403
Markdown (Informal)
[BERT-based distractor generation for Swedish reading comprehension questions using a small-scale dataset](https://aclanthology.org/2021.inlg-1.43) (Kalpakchi & Boye, INLG 2021)
ACL