Feature Optimization for Predicting Readability of Arabic L1 and L2

Hind Saddiki, Nizar Habash, Violetta Cavalli-Sforza, Muhamed Al Khalil

Research output: Contribution to conferencePaper

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.
Original languageEnglish (US)
Number of pages10
DOIs
StatePublished - Jun 29 2018

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