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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Reading Between the Lines: A dataset and a study on why some texts are tougher than others
%A Khallaf, Nouran
%A Eugeni, Carlo
%A Sharoff, Serge
%Y Zock, Michael
%Y Inui, Kentaro
%Y Yuan, Zheng
%S Proceedings of the First Workshop on Writing Aids at the Crossroads of AI, Cognitive Science and NLP (WRAICOGS 2025)
%D 2025
%8 January
%I International Committee on Computational Linguistics
%C Abu Dhabi, UAE
%F khallaf-etal-2025-reading
%X 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.
%U https://aclanthology.org/2025.wraicogs-1.3/
%P 24-34

Markdown (Informal)

[Reading Between the Lines: A dataset and a study on why some texts are tougher than others](https://aclanthology.org/2025.wraicogs-1.3/) (Khallaf et al., WRAICOGS 2025)

• Reading Between the Lines: A dataset and a study on why some texts are tougher than others (Khallaf et al., WRAICOGS 2025)

ACL

• Nouran Khallaf, Carlo Eugeni, and Serge Sharoff. 2025. Reading Between the Lines: A dataset and a study on why some texts are tougher than others. In Proceedings of the First Workshop on Writing Aids at the Crossroads of AI, Cognitive Science and NLP (WRAICOGS 2025), pages 24–34, Abu Dhabi, UAE. International Committee on Computational Linguistics.