Arabic words are often ambiguous between name and non-name interpretations, frequently leading to incorrect name translations. We present a technique to disambiguate and transliterate names even if name interpretations do not exist or have relatively low probability distributions in the parallel training corpus. The key idea comprises named entity classing at the preprocessing step, decoding of a simple confusion network created from the name class label and the input word at the statistical machine translation step, and transliteration of names at the post-processing step. Human evaluations indicate that the proposed technique leads to a statistically significant translation quality improvement of highly ambiguous evaluation data sets without degrading the translation quality of a data set with very few names.
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