@inproceedings{ngo-parmentier-2023-towards,
    title = "Towards Sentence-level Text Readability Assessment for {F}rench",
    author = "Ngo, Duy Van  and
      Parmentier, Yannick",
    editor = "{\v{S}}tajner, Sanja  and
      Saggio, Horacio  and
      Shardlow, Matthew  and
      Alva-Manchego, Fernando",
    booktitle = "Proceedings of the Second Workshop on Text Simplification, Accessibility and Readability",
    month = sep,
    year = "2023",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
    url = "https://preview.aclanthology.org/ingest-emnlp/2023.tsar-1.8/",
    pages = "78--84",
    abstract = "In this paper, we report on some experiments aimed at exploring the relation between document-level and sentence-level readability assessment for French. These were run on an open-source tailored corpus, which was automatically created by aggregating various sources from children{'}s literature. On top of providing the research community with a freely available corpus, we report on sentence readability scores obtained when applying both classical approaches (aka readability formulas) and state-of-the-art deep learning techniques (e.g. fine-tuning of large language models). Results show a relatively strong correlation between document-level and sentence-level readability, suggesting ways to reduce the cost of building annotated sentence-level readability datasets."
}