  In this paper, we introduce a novel approach to generate synthetic data for
training Neural Machine Translation systems. The proposed approach transforms a
given parallel corpus between a written language and a target language to a
parallel corpus between a spoken dialect variant and the target language. Our
approach is language independent and can be used to generate data for any
variant of the source language such as slang or spoken dialect or even for a
different language that is closely related to the source language.
  The proposed approach is based on local embedding projection of distributed
representations which utilizes monolingual embeddings to transform parallel
data across language variants. We report experimental results on Levantine to
English translation using Neural Machine Translation. We show that the
generated data can improve a very large scale system by more than 2.8 Bleu
points using synthetic spoken data which shows that it can be used to provide a
reliable translation system for a spoken dialect that does not have sufficient
parallel data.
