Assessing French Readability for Adults with Low Literacy: A Global and Local Perspective
Wafa Aissa, Thibault Ba{\~n}eras-Roux, Elodie Vanzeveren, Lingyun Gao, Rodrigo Wilkens, Thomas Fran{\c{c}}ois
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.
BibTeX
@inproceedings{emnlp2025_assessingfrenchr,
title = {Assessing French Readability for Adults with Low Literacy: A Global and Local Perspective},
author = {Wafa Aissa and Thibault Ba{\~n}eras-Roux and Elodie Vanzeveren and Lingyun Gao and Rodrigo Wilkens and Thomas Fran{\c{c}}ois},
booktitle = {EMNLP 2025},
year = {2025}
}