Abstract
Language proficiency tests are used to evaluate and compare the progress of language learners. We present an approach for automatic difficulty prediction of C-tests that performs on par with human experts. On the basis of detailed analysis of newly collected data, we develop a model for C-test difficulty introducing four dimensions: solution difficulty, candidate ambiguity, inter-gap dependency, and paragraph difficulty. We show that cues from all four dimensions contribute to C-test difficulty.- Anthology ID:
- Q14-1040
- Volume:
- Transactions of the Association for Computational Linguistics, Volume 2
- Month:
- Year:
- 2014
- Address:
- Cambridge, MA
- Editors:
- Dekang Lin, Michael Collins, Lillian Lee
- Venue:
- TACL
- SIG:
- Publisher:
- MIT Press
- Note:
- Pages:
- 517–530
- Language:
- URL:
- https://aclanthology.org/Q14-1040
- DOI:
- 10.1162/tacl_a_00200
- Cite (ACL):
- Lisa Beinborn, Torsten Zesch, and Iryna Gurevych. 2014. Predicting the Difficulty of Language Proficiency Tests. Transactions of the Association for Computational Linguistics, 2:517–530.
- Cite (Informal):
- Predicting the Difficulty of Language Proficiency Tests (Beinborn et al., TACL 2014)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/Q14-1040.pdf