@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/ingest-emnlp/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."
}