Generating Adequate Distractors for Multiple-Choice Questions
This addresses the need for efficient question generation in educational testing, though it is incremental as it combines existing techniques.
The paper tackles the problem of automatically generating adequate distractors for multiple-choice questions derived from articles, achieving that 84% of generated questions have three adequate distractors.
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.