Abstract
We consider the problem of automatically suggesting distractors for multiple-choice cloze questions designed for second-language learners. We describe the creation of a dataset including collecting manual annotations for distractor selection. We assess the relationship between the choices of the annotators and features based on distractors and the correct answers, both with and without the surrounding passage context in the cloze questions. Simple features of the distractor and correct answer correlate with the annotations, though we find substantial benefit to additionally using large-scale pretrained models to measure the fit of the distractor in the context. Based on these analyses, we propose and train models to automatically select distractors, and measure the importance of model components quantitatively.- Anthology ID:
- 2020.bea-1.10
- Volume:
- Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications
- Month:
- July
- Year:
- 2020
- Address:
- Seattle, WA, USA → Online
- Editors:
- Jill Burstein, Ekaterina Kochmar, Claudia Leacock, Nitin Madnani, Ildikó Pilán, Helen Yannakoudakis, Torsten Zesch
- Venue:
- BEA
- SIG:
- SIGEDU
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 102–114
- Language:
- URL:
- https://aclanthology.org/2020.bea-1.10
- DOI:
- 10.18653/v1/2020.bea-1.10
- Cite (ACL):
- Lingyu Gao, Kevin Gimpel, and Arnar Jensson. 2020. Distractor Analysis and Selection for Multiple-Choice Cloze Questions for Second-Language Learners. In Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 102–114, Seattle, WA, USA → Online. Association for Computational Linguistics.
- Cite (Informal):
- Distractor Analysis and Selection for Multiple-Choice Cloze Questions for Second-Language Learners (Gao et al., BEA 2020)
- PDF:
- https://preview.aclanthology.org/landing_page/2020.bea-1.10.pdf