Distractor Generation for Multiple-Choice Questions: A Survey of
  Methods, Datasets, and Evaluation
Elaf Alhazmi Quan Z. Sheng Wei Emma Zhang Munazza Zaib Ahoud Alhazmi
Distractors are important in learning evaluation. This paper surveys
distractor generation tasks using English multiple-choice question datasets for
textual and multimodal contexts. In particular, this paper presents a thorough
literature review of the recent studies on distractor generation tasks,
discusses multiple choice components and their characteristics, analyzes the
related datasets, and summarizes the evaluation metrics of distractor
generation. Our investigation reveals that more than half of datasets are
human-generated from educational sources in specific domains such as Science
and English, which are largely text-based, with a lack of open domain and
multimodal datasets.