Selecting Context Clozes for Lightweight Reading Compliance

Greg Keim, Michael Littman


Abstract
We explore a novel approach to reading compliance, leveraging large language models to select inline challenges that discourage skipping during reading. This lightweight ‘testing’ is accomplished through automatically identified context clozes where the reader must supply a missing word that would be hard to guess if earlier material was skipped. Clozes are selected by scoring each word by the contrast between its likelihood with and without prior sentences as context, preferring to leave gaps where this contrast is high. We report results of an initial human-participant test that indicates this method can find clozes that have this property.
Anthology ID:
2022.bea-1.21
Volume:
Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2022)
Month:
July
Year:
2022
Address:
Seattle, Washington
Venues:
BEA | NAACL
SIG:
SIGEDU
Publisher:
Association for Computational Linguistics
Note:
Pages:
167–172
Language:
URL:
https://aclanthology.org/2022.bea-1.21
DOI:
10.18653/v1/2022.bea-1.21
Bibkey:
Cite (ACL):
Greg Keim and Michael Littman. 2022. Selecting Context Clozes for Lightweight Reading Compliance. In Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2022), pages 167–172, Seattle, Washington. Association for Computational Linguistics.
Cite (Informal):
Selecting Context Clozes for Lightweight Reading Compliance (Keim & Littman, BEA 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.bea-1.21.pdf