@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://preview.aclanthology.org/jlcl-multiple-ingestion/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."
}
Markdown (Informal)
[BERT-based distractor generation for Swedish reading comprehension questions using a small-scale dataset](https://preview.aclanthology.org/jlcl-multiple-ingestion/2021.inlg-1.43/) (Kalpakchi & Boye, INLG 2021)
ACL