Multiple-choice Question Generation for the Chinese Language

Yicheng Sun, Hejia Chen, Jie Wang

2022

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.

Paper Citation

in Harvard Style

Sun Y., Chen H. and Wang J. (2022). Multiple-choice Question Generation for the Chinese Language. In Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 1: KDIR; ISBN 978-989-758-614-9, SciTePress, pages 345-354. DOI: 10.5220/0011589800003335

in Bibtex Style

@conference{kdir22,
author={Yicheng Sun and Hejia Chen and Jie Wang},
title={Multiple-choice Question Generation for the Chinese Language},
booktitle={Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 1: KDIR},
year={2022},
pages={345-354},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011589800003335},
isbn={978-989-758-614-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 1: KDIR
TI - Multiple-choice Question Generation for the Chinese Language
SN - 978-989-758-614-9
AU - Sun Y.
AU - Chen H.
AU - Wang J.
PY - 2022
SP - 345
EP - 354
DO - 10.5220/0011589800003335
PB - SciTePress