Improving Text Readability through Segmentation into Rheses

Antoine Jamelot, Solen Quiniou, Sophie Hamon


Abstract
Enhancing text readability is crucial for readers with challenges like dyslexia. This paper delves into the segmentation of sentences into rheses, i.e. rhythmic and semantic units. Their aim is to clarify sentence structures for improved comprehension, through a harmonious balance between syntactic accuracy, the natural rhythm of reading aloud, and the delineation of meaningful units. This study relates and compares our various attempts to improve a pre-existing rhesis segmentation tool, which is based on the selection of candidate segmentations. We also release TeRheSe (Texts with Rhesis Segmentation), a bilingual dataset, segmented into rheses, comprising 12 books from classic literature in French and English. We evaluated our approaches on this dataset, showing the efficiency of a novel approach based on token classification, reaching a F1-score of 90.0% in English (previously 85.3%) and 91.3% in French (previously 88.0%). We also study the potential of leveraging prosodic elements, though its definitive impact remains inconclusive.
Anthology ID:
2024.lrec-main.781
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
8925–8930
Language:
URL:
https://aclanthology.org/2024.lrec-main.781
DOI:
Bibkey:
Cite (ACL):
Antoine Jamelot, Solen Quiniou, and Sophie Hamon. 2024. Improving Text Readability through Segmentation into Rheses. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 8925–8930, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Improving Text Readability through Segmentation into Rheses (Jamelot et al., LREC-COLING 2024)
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PDF:
https://preview.aclanthology.org/nschneid-patch-2/2024.lrec-main.781.pdf
Optional supplementary material:
 2024.lrec-main.781.OptionalSupplementaryMaterial.zip