Towards Sentence-level Text Readability Assessment for French

Duy Van Ngo (1) , Yannick Parmentier (1, 2)

1 SYNALP - Natural Language Processing : representations, inference and semantics
2 LIFO - Laboratoire d'Informatique Fondamentale d'Orléans

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.

Mots clés: text readability; French; machine learning; deep learning

Domaines: Informatique et langage [cs.CL]; Intelligence artificielle [cs.AI]

Duy Van Ngo, Yannick Parmentier. Towards Sentence-level Text Readability Assessment for French. Second Workshop on Text Simplification, Accessibility and Readability (TSAR@RANLP2023), Sep 2023, Varna, Bulgaria. ⟨hal-04192063⟩