The paper introduces Ace-CEFR, a dataset of short conversational English texts with expert CEFR labels, and shows that a fine-tuned BERT model predicts these labels more accurately than a single human expert.
CEFR-Based Sentence Difficulty Annotation and Assessment
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
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.
fields
cs.CL 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Ace-CEFR -- A Dataset for Automated Evaluation of the Linguistic Difficulty of Conversational Texts for LLM Applications
The paper introduces Ace-CEFR, a dataset of short conversational English texts with expert CEFR labels, and shows that a fine-tuned BERT model predicts these labels more accurately than a single human expert.