# Generating Adequate Distractors for Multiple-Choice Questions

arXiv: https://arxiv.org/abs/2010.12658 · HTML: https://mlsanity.com/paper/2010.12658/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2020.

## Built on by (most cited first, 1 of 1)

- [Automatic Generation of Multiple-Choice Questions](https://mlsanity.com/paper/2303.14576/) (2023)

## Abstract (arXiv)

This paper presents a novel approach to automatic generation of adequate distractors for a given question-answer pair (QAP) generated from a given article to form an adequate multiple-choice question (MCQ). Our method is a combination of part-of-speech tagging, named-entity tagging, semantic-role labeling, regular expressions, domain knowledge bases, word embeddings, word edit distance, WordNet, and other algorithms. We use the US SAT (Scholastic Assessment Test) practice reading tests as a dataset to produce QAPs and generate three distractors for each QAP to form an MCQ. We show that, via experiments and evaluations by human judges, each MCQ has at least one adequate distractor and 84% of MCQs have three adequate distractors.