@inproceedings{arase-etal-2022-cefr,
    title = "{CEFR}-Based Sentence Difficulty Annotation and Assessment",
    author = "Arase, Yuki  and
      Uchida, Satoru  and
      Kajiwara, Tomoyuki",
    editor = "Goldberg, Yoav  and
      Kozareva, Zornitsa  and
      Zhang, Yue",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Author-page-Marten-During-lu/2022.emnlp-main.416/",
    doi = "10.18653/v1/2022.emnlp-main.416",
    pages = "6206--6219",
    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."
}