Authors:
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, P.R.C.
Keyword(s):
Multiple-choice Question Generation, Natural Language Processing.
Abstract:
We present a method to generate multiple-choice questions (MCQs) from Chinese texts for factual, eventual, and causal answer keys. We first identify answer keys of these types using NLP tools and regular expressions. We then transform declarative sentences into interrogative sentences, and generate three distractors using geographic and aliased entity knowledge bases, Synonyms, HowNet, and word embeddings. We show that our method can generate adequate questions on three of the four reported cases that the SOTA model has failed. Moreover, on a dataset of 100 articles randomly selected from a Chinese Wikipedia data dump, our method generates a total of 3,126 MCQs. Three well-educated native Chinese speakers evaluate these MCQs and confirm that 76% of MCQs, 85% of question-answer paris, and 91% of questions are adequate and 96.5% of MCQs are acceptable.