Abstract: Recurrent neural networks are powerful tools giving excellent results in various tasks, including Natural Language Processing tasks. In this paper, we use Gated Recurrent Unit, a recurrent neural network implementing a simple gating mechanism in order to improve the diacritization process of Arabic. Evaluation of Gated Recurrent Unit for diacritization is performed in comparison with the state-of-the art results obtained with Long-Short term memory a powerful RNN architecture giving the best-known results in diacritization. Evaluation covers two performance aspects, Error rate and training runtime.

Rajae Moumen, Raddouane Chiheb, Rdouan Faizi and Abdellatif El Afia, “Evaluation of Gated Recurrent Unit in Arabic Diacritization” International Journal of Advanced Computer Science and Applications(IJACSA), 9(11), 2018. http://dx.doi.org/10.14569/IJACSA.2018.091150

@article{Moumen2018,
title = {Evaluation of Gated Recurrent Unit in Arabic Diacritization},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2018.091150},
url = {http://dx.doi.org/10.14569/IJACSA.2018.091150},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {11},
author = {Rajae Moumen and Raddouane Chiheb and Rdouan Faizi and Abdellatif El Afia}
}