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
Venues:
ACL | NLP-TEA | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
20–29
Language:
URL:
https://www.aclweb.org/anthology/W18-3703
DOI:
10.18653/v1/W18-3703
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-3703.pdf