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
- 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)
- PDF:
- https://preview.aclanthology.org/ml4al-ingestion/W17-5015.pdf