  Diacritics (short vowels) are typically omitted when writing Arabic text, and
readers have to reintroduce them to correctly pronounce words. There are two
types of Arabic diacritics: the first are core-word diacritics (CW), which
specify the lexical selection, and the second are case endings (CE), which
typically appear at the end of the word stem and generally specify their
syntactic roles. Recovering CEs is relatively harder than recovering core-word
diacritics due to inter-word dependencies, which are often distant. In this
paper, we use a feature-rich recurrent neural network model that uses a variety
of linguistic and surface-level features to recover both core word diacritics
and case endings. Our model surpasses all previous state-of-the-art systems
with a CW error rate (CWER) of 2.86\% and a CE error rate (CEER) of 3.7% for
Modern Standard Arabic (MSA) and CWER of 2.2% and CEER of 2.5% for Classical
Arabic (CA). When combining diacritized word cores with case endings, the
resultant word error rate is 6.0% and 4.3% for MSA and CA respectively. This
highlights the effectiveness of feature engineering for such deep neural
models.
