Cloze Quality Estimation for Language Assessment

Zizheng Zhang, Masato Mita, Mamoru Komachi


Abstract
Cloze tests play an essential role in language assessment and help language learners improve their skills. In this paper, we propose a novel task called Cloze Quality Estimation (CQE) — a zero-shot task of evaluating whether a cloze test is of sufficient “high-quality” for language assessment based on two important factors: reliability and validity. We have taken the first step by creating a new dataset named CELA for the CQE task, which includes English cloze tests and corresponding evaluations about their quality annotated by native English speakers, which includes 2,597 and 1,730 instances in aspects of reliability and validity, respectively. We have tested baseline evaluation methods on the dataset, showing that our method could contribute to the CQE task, but the task is still challenging.
Anthology ID:
2023.findings-eacl.39
Volume:
Findings of the Association for Computational Linguistics: EACL 2023
Month:
May
Year:
2023
Address:
Dubrovnik, Croatia
Editors:
Andreas Vlachos, Isabelle Augenstein
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
540–550
Language:
URL:
https://aclanthology.org/2023.findings-eacl.39
DOI:
10.18653/v1/2023.findings-eacl.39
Bibkey:
Cite (ACL):
Zizheng Zhang, Masato Mita, and Mamoru Komachi. 2023. Cloze Quality Estimation for Language Assessment. In Findings of the Association for Computational Linguistics: EACL 2023, pages 540–550, Dubrovnik, Croatia. Association for Computational Linguistics.
Cite (Informal):
Cloze Quality Estimation for Language Assessment (Zhang et al., Findings 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.findings-eacl.39.pdf
Video:
 https://aclanthology.org/2023.findings-eacl.39.mp4