Advances in automatic readability assessment can impact the way people consume information in a number of domains. Arabic, being a low-resource and morphologically complex language, presents numerous challenges to the task of automatic readability assessment. In this paper, we present the largest and most in-depth computational readability study for Arabic to date. We study a large set of features with varying depths, from shallow words to syntactic trees, for both L1 and L2 readability tasks. Our best L1 readability accuracy result is 94.8% (75% error reduction from a commonly used baseline). The comparable results for L2 are 72.4% (45% error reduction). We also demonstrate the added value of leveraging L1 features for L2 readability prediction.
Saddiki, H., Habash, N., Cavalli-Sforza, V., & Al Khalil, M. (2019). Feature Optimization for Predicting Readability of Arabic L1 and L2 (pp. 20–29). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/w18-3703