Inclusive Easy-to-Read Generation for Individuals with Cognitive Impairments
This addresses accessibility challenges in healthcare, education, and civic life for people with cognitive impairments, but it is incremental as it builds on existing methods with a new dataset.
The paper tackled the problem of generating Easy-to-Read text for individuals with cognitive impairments by creating the first dataset compliant with European guidelines and fine-tuning models, with results showing that pre-trained language models perform comparably to large language models and adapt well to out-of-domain texts.
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.