Strategies for Arabic Readability Modeling
Juan Piñeros Liberato, Bashar Alhafni, Muhamed Al Khalil, Nizar Y. Habash · 2024
Automatic readability assessment is relevant to building NLP applications for education, content analysis, and accessibility.However, Arabic readability assessment is a challenging task due to Arabic's morphological richness and limited readability resources.In this paper, we present a set of experimental results on Arabic readability assessment using a diverse range of approaches, from rule-based methods to Arabic pretrained language models.We report our results on a newly created corpus at different textual granularity levels (words and sentence fragments).Our results show that combining different techniques yields the best results, achieving an overall macro F 1 score of 86.7 at the word level and 87.9 at the fragment level on a blind test set.We make our code, data, and pretrained models publicly available.1