Several people are excluded from democratic deliberation because the language which is used in this context may be too difficult to understand for them. Our iDEM project aims at lowering existing linguistic barriers in deliberative processes by developing technology to facilitate the translation of complicated text into easy to read formats which are more suitable for may people. In this paper we describe classification experiments for detecting different types of difficulties which should be amended in order to make texts easier to understand. We focus on a lexical simplification system which can achieve state-of-the-art results with the use of a free and open-weight Large Language Model for the Romance Languages in the iDEM project. Moreover, a sentence segmentation system is introduced that can create text segmentation for long sentences based on training data. We describe the iDEM mobile app, which will make our technology available as a service for end-users of our target populations.

Our research aims at better understanding what makes a text difficult to read for specific audiences with intellectual disabilities, more specifically, people who have limitations in cognitive functioning, such as reading and understanding skills, an IQ below 70, and challenges in conceptual domains. We introduce a scheme for the annotation of difficulties which is based on empirical research in psychology as well as on research in translation studies. The paper describes the annotated dataset, primarily derived from the parallel texts (standard English and Easy to Read English translations) made available online. we fine-tuned four different pre-trained transformer models to perform the task of multiclass classification to predict the strategies required for simplification. We also investigate the possibility to interpret the decisions of this language model when it is aimed at predicting the difficulty of sentences in this dataset.