Arabic is mostly written now without its diacritics (short vowels). Adding these diacritics decreases reading
ambiguity among other benefits. This work aims to develop a fast and accurate machine learning solution to
diacritize Arabic text automatically. This paper uses long short-term memory (LSTM) recurrent neural networks
to diacritize Arabic text. Intensive experiments are performed to evaluate proposed alternative design and data
encoding options towards a fast and accurate solution. Our experiments involve investigating and handling
problems in sequence lengths, proposing and evaluating alternative encodings of the diacritized output sequences
and tuning and evaluating neural network options including architecture, network size and hyper-parameters.
This paper recommends a solution that can be fast trained on a large dataset and uses four bidirectional LSTM
layers to predict the diacritics of the input sequence of Arabic letters. This solution achieves a diacritization error
rate of 2.46% on the LDC ATB3 dataset benchmark and 1.97% on the larger new Tashkeela dataset. This latter
rate is 47% improvement over the best-published previous result.
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