Authors:
Cheng Zhang
1
;
Yicheng Sun
1
;
Hejia Chen
2
and
Jie Wang
1
Affiliations:
1
Department of Computer Science, University of Massachusetts, Lowell, MA 01854, U.S.A.
;
2
School of Computer Science and Technology, Xidian University, Xi’an 710126, P.R. China
Keyword(s):
Multiple-Choice Questions, Distractors, Word Embeddings, Word Edit Distance.
Abstract:
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.