2025/01/03 by Nouran Khallaf, Khallaf, Nouran, Carlo Eugeni +3 · 1 voice
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Natural Language Processing Techniques #cs.CL
paper · pdf · doi:10.48550/arxiv.2501.01796
openalex publication_date 2025/01/03 · arxiv published 2025/01/03 · arxiv updated 2025/01/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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. The resources are available from https://github.com/Nouran-Khallaf/why-tough