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We trained a model to automatically transliterate Judeo-Arabic texts intoArabic script, enabling Arabic readers to access those writings. We employ arecurrent neural network (RNN), combined with the connectionist temporalclassification (CTC) loss to deal with unequal input/output lengths. Thisobligates adjustments in the training data to avoid input sequences that areshorter than their corresponding outputs. We also utilize a pretraining stagewith a different loss function to improve network converge. Since only a singlesource of parallel text was available for training, we take advantage of thepossibility of generating data synthetically. We train a model that has thecapability to memorize words in the output language, and that also utilizescontext for distinguishing ambiguities in the transliteration. We obtain animprovement over the baseline 9.5% character error, achieving 2% error with ourbest configuration. To measure the contribution of context to learning, we alsotested word-shuffled data, for which the error rises to 2.5%.

Transliteration of Judeo-Arabic Texts into Arabic Script Using Recurrent Neural Networks