Reading Between the Lines: A dataset and a study on why some texts are tougher than others
This work addresses text accessibility for people with intellectual disabilities, but it is incremental as it builds on existing translation studies and pre-trained models.
The study tackled the problem of understanding text difficulty for audiences with intellectual disabilities by introducing an annotation scheme and dataset from parallel English texts, and fine-tuned transformer models achieved multiclass classification for simplification strategies.
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