@inproceedings{aissa-etal-2025-assessing,
    title = "Assessing {F}rench Readability for Adults with Low Literacy: A Global and Local Perspective",
    author = "Aissa, Wafa  and
      Ba{\~n}eras-Roux, Thibault  and
      Vanzeveren, Elodie  and
      Gao, Lingyun  and
      Wilkens, Rodrigo  and
      Fran{\c{c}}ois, Thomas",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1036/",
    pages = "20528--20550",
    ISBN = "979-8-89176-332-6",
    abstract = "This study presents a novel approach to assessing French text readability for adults with low literacy skills, addressing both global (full-text) and local (segment-level) difficulty. We introduce a dataset of 461 texts annotated using a difficulty scale developed specifically for this population. Using this corpus, we conducted a systematic comparison of key readability modeling approaches, including machine learning techniques based on linguistic variables, fine-tuning of CamemBERT, a hybrid approach combining CamemBERT with linguistic variables, and the use of generative language models (LLMs) to carry out readability assessment at both global and local levels."
}