@inproceedings{saddiki-etal-2018-feature,
    title = "Feature Optimization for Predicting Readability of {A}rabic {L}1 and {L}2",
    author = "Saddiki, Hind  and
      Habash, Nizar  and
      Cavalli-Sforza, Violetta  and
      Al Khalil, Muhamed",
    booktitle = "Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W18-3703",
    doi = "10.18653/v1/W18-3703",
    pages = "20--29",
    abstract = "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.",
}