Inclusive Easy-to-Read Generation for Individuals with Cognitive Impairments
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arXiv
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| Autori principali: | , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866914070080258048 |
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| author | Ledoyen, François Dias, Gaël Lechervy, Alexis Pantin, Jeremie Maurel, Fabrice Chahir, Youssef Gouzonnat, Elisa Berthelot, Mélanie Moravac, Stanislas Altinier, Armony Khairalla, Amy |
| author_facet | Ledoyen, François Dias, Gaël Lechervy, Alexis Pantin, Jeremie Maurel, Fabrice Chahir, Youssef Gouzonnat, Elisa Berthelot, Mélanie Moravac, Stanislas Altinier, Armony Khairalla, Amy |
| contents | Ensuring accessibility for individuals with cognitive impairments is essential for autonomy, self-determination, and full citizenship. However, manual Easy-to-Read (ETR) text adaptations are slow, costly, and difficult to scale, limiting access to crucial information in healthcare, education, and civic life. AI-driven ETR generation offers a scalable solution but faces key challenges, including dataset scarcity, domain adaptation, and balancing lightweight learning of Large Language Models (LLMs). In this paper, we introduce ETR-fr, the first dataset for ETR text generation fully compliant with European ETR guidelines. We implement parameter-efficient fine-tuning on PLMs and LLMs to establish generative baselines. To ensure high-quality and accessible outputs, we introduce an evaluation framework based on automatic metrics supplemented by human assessments. The latter is conducted using a 36-question evaluation form that is aligned with the guidelines. Overall results show that PLMs perform comparably to LLMs and adapt effectively to out-of-domain texts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_00691 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Inclusive Easy-to-Read Generation for Individuals with Cognitive Impairments Ledoyen, François Dias, Gaël Lechervy, Alexis Pantin, Jeremie Maurel, Fabrice Chahir, Youssef Gouzonnat, Elisa Berthelot, Mélanie Moravac, Stanislas Altinier, Armony Khairalla, Amy Computation and Language Artificial Intelligence Ensuring accessibility for individuals with cognitive impairments is essential for autonomy, self-determination, and full citizenship. However, manual Easy-to-Read (ETR) text adaptations are slow, costly, and difficult to scale, limiting access to crucial information in healthcare, education, and civic life. AI-driven ETR generation offers a scalable solution but faces key challenges, including dataset scarcity, domain adaptation, and balancing lightweight learning of Large Language Models (LLMs). In this paper, we introduce ETR-fr, the first dataset for ETR text generation fully compliant with European ETR guidelines. We implement parameter-efficient fine-tuning on PLMs and LLMs to establish generative baselines. To ensure high-quality and accessible outputs, we introduce an evaluation framework based on automatic metrics supplemented by human assessments. The latter is conducted using a 36-question evaluation form that is aligned with the guidelines. Overall results show that PLMs perform comparably to LLMs and adapt effectively to out-of-domain texts. |
| title | Inclusive Easy-to-Read Generation for Individuals with Cognitive Impairments |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2510.00691 |