Year: 2024
Type: conference-paper
Abstract: This research introduces DARES, a dataset for assessing the readability of Arabic text in Saudi school materials.DARES compromise of 13,335 instances from textbooks used in 2021 and contains two subtasks; (a) Coarse-grained readability assessment where the text is classified into different educational levels such as primary and secondary.(b) Fine-grained readability assessment where the text is classified into individual grades.We fine-tuned five transformer models that support Arabic and found that CAMeLBERTmix performed better in all input settings.Evaluation results showed high performance for the coarse-grained readability assessment task, achieving a weighted F1 score of 0.91 and a macro F1 score of 0.79.The fine-grained task achieved a weighted F1 score of 0.68 and a macro F1 score of 0.55.These findings demonstrate the potential of our approach for advancing Arabic text readability assessment in education, with implications for future innovations in the field.
Institutions: Lancaster University, King Saud University, Aston University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W7131777546