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

Many of the great Jewish works of the Middle Ages were written in Judeo-Arabic, a Jewish branch of the Arabic language family that incorporates the Hebrew script as its writing system. In this work we are trying to train a model that will automatically transliterate Judeo-Arabic into Arabic script; thus we aspire to enable Arabic readers to access those writings. We adopt a recurrent neural network (RNN) approach to the problem, applying connectionist temporal classification loss to deal with unequal input/output lengths. This choice obligates adjustments, termed doubling, in the training data to avoid input sequences that are shorter than their corresponding outputs. We also utilize a pretraining stage with a different loss function to help the network converge. Furthermore, since only a single source of parallel text was available for training, we examine the possibility of generating data synthetically from other Arabic original text from the time in question, leveraging the fact that, though the convention for mapping applied by the Judeo-Arabic author has a one-to-many relation from Judeo-Arabic to Arabic, its reverse (from Arabic to Judeo-Arabic) is a proper function. By this we attempt to train a model that has the capability to memorize words in the output language, and that also utilizes the context for distinguishing ambiguities in the transliteration. We examine this ability by testing on shuffled data that lacks context. We obtain an improvement over the baseline results (9.5% error), achieving 2% error with our system. On the shuffled test data, the error rises to 2.5%.