Distractor Generation for Chinese Fill-in-the-blank Items

Shu Jiang, John Lee


Abstract
This paper reports the first study on automatic generation of distractors for fill-in-the-blank items for learning Chinese vocabulary. We investigate the quality of distractors generated by a number of criteria, including part-of-speech, difficulty level, spelling, word co-occurrence and semantic similarity. Evaluations show that a semantic similarity measure, based on the word2vec model, yields distractors that are significantly more plausible than those generated by baseline methods.
Anthology ID:
W17-5015
Volume:
Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Editors:
Joel Tetreault, Jill Burstein, Claudia Leacock, Helen Yannakoudakis
Venue:
BEA
SIG:
SIGEDU
Publisher:
Association for Computational Linguistics
Note:
Pages:
143–148
Language:
URL:
https://aclanthology.org/W17-5015
DOI:
10.18653/v1/W17-5015
Bibkey:
Cite (ACL):
Shu Jiang and John Lee. 2017. Distractor Generation for Chinese Fill-in-the-blank Items. In Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications, pages 143–148, Copenhagen, Denmark. Association for Computational Linguistics.
Cite (Informal):
Distractor Generation for Chinese Fill-in-the-blank Items (Jiang & Lee, BEA 2017)
Copy Citation:
PDF:
https://aclanthology.org/W17-5015.pdf