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            <namePart type="given">Giorgio</namePart>
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    <abstract>This research introduces DARES, a dataset for assessing the readability of Arabic text in Saudi school materials. DARES compromise of 13335 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 the best 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.</abstract>
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%0 Conference Proceedings
%T DARES: Dataset for Arabic Readability Estimation of School Materials
%A El-Haj, Mo
%A Almujaiwel, Sultan
%A Premasiri, Damith
%A Ranasinghe, Tharindu
%A Mitkov, Ruslan
%Y Nunzio, Giorgio Maria Di
%Y Vezzani, Federica
%Y Ermakova, Liana
%Y Azarbonyad, Hosein
%Y Kamps, Jaap
%S Proceedings of the Workshop on DeTermIt! Evaluating Text Difficulty in a Multilingual Context @ LREC-COLING 2024
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F el-haj-etal-2024-dares
%X This research introduces DARES, a dataset for assessing the readability of Arabic text in Saudi school materials. DARES compromise of 13335 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 the best 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.
%U https://aclanthology.org/2024.determit-1.10
%P 103-113

Markdown (Informal)

[DARES: Dataset for Arabic Readability Estimation of School Materials](https://aclanthology.org/2024.determit-1.10) (El-Haj et al., DeTermIt-WS 2024)

• DARES: Dataset for Arabic Readability Estimation of School Materials (El-Haj et al., DeTermIt-WS 2024)

ACL

• Mo El-Haj, Sultan Almujaiwel, Damith Premasiri, Tharindu Ranasinghe, and Ruslan Mitkov. 2024. DARES: Dataset for Arabic Readability Estimation of School Materials. In Proceedings of the Workshop on DeTermIt! Evaluating Text Difficulty in a Multilingual Context @ LREC-COLING 2024, pages 103–113, Torino, Italia. ELRA and ICCL.