Feature Optimization for Predicting Readability of Arabic L1 and L2

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

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.
Anthology ID:
W18-3703
Volume:
Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications
Month:
July
Year:
2018
Address:
Melbourne, Australia
Editors:
Yuen-Hsien Tseng, Hsin-Hsi Chen, Vincent Ng, Mamoru Komachi
Venue:
NLP-TEA
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
20–29
Language:
URL:
https://aclanthology.org/W18-3703
DOI:
10.18653/v1/W18-3703
Bibkey:
Cite (ACL):
Hind Saddiki, Nizar Habash, Violetta Cavalli-Sforza, and Muhamed Al Khalil. 2018. Feature Optimization for Predicting Readability of Arabic L1 and L2. In Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications, pages 20–29, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Feature Optimization for Predicting Readability of Arabic L1 and L2 (Saddiki et al., NLP-TEA 2018)
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PDF:
https://preview.aclanthology.org/nschneid-patch-4/W18-3703.pdf
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