  In this work, we present several deep learning models for the automatic
diacritization of Arabic text. Our models are built using two main approaches,
viz. Feed-Forward Neural Network (FFNN) and Recurrent Neural Network (RNN),
with several enhancements such as 100-hot encoding, embeddings, Conditional
Random Field (CRF) and Block-Normalized Gradient (BNG). The models are tested
on the only freely available benchmark dataset and the results show that our
models are either better or on par with other models, which require
language-dependent post-processing steps, unlike ours. Moreover, we show that
diacritics in Arabic can be used to enhance the models of NLP tasks such as
Machine Translation (MT) by proposing the Translation over Diacritization (ToD)
approach.
