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CEFR-Based Sentence Difficulty Annotation and Assessment

Authors :
Arase, Yuki
Uchida, Satoru
Kajiwara, Tomoyuki
Publication Year :
2022

Abstract

Controllable text simplification is a crucial assistive technique for language learning and teaching. One of the primary factors hindering its advancement is the lack of a corpus annotated with sentence difficulty levels based on language ability descriptions. To address this problem, we created the CEFR-based Sentence Profile (CEFR-SP) corpus, containing 17k English sentences annotated with the levels based on the Common European Framework of Reference for Languages assigned by English-education professionals. In addition, we propose a sentence-level assessment model to handle unbalanced level distribution because the most basic and highly proficient sentences are naturally scarce. In the experiments in this study, our method achieved a macro-F1 score of 84.5% in the level assessment, thus outperforming strong baselines employed in readability assessment.<br />Comment: EMNLP 2022

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2210.11766
Document Type :
Working Paper