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.

5. Data Generation
Illustration

7. Parallel Data between S and I
Seed lexicons or seed parallel data between S and I (Optional)
Seed Lexicons or seed parallel data between I and E
Monolingual corpora for S , I and E

11. Three-Way
Semantic
Projection
Intermediate I
Output
Dialect E
Input S

12. Model BLEU (S2S_Test)
MSA Large System: includes all Levantine parallel data 25.03
MSA Large System + Adaptation using Levantine Data 27.37
MSA Large System + Lev GenCorpus 27.91
Oracle (Human translation to MSA+ MSA Large System) 28.20
Model BLEU (CallHome_Test)
MSA Large System: includes subset of Egyptian parallel data 15.7
Egyptian Baseline: Trained with 210K parallel Egyptian data 19.3
Egyptian: Trained with 210K parallel data + EG GenCorpus 19.7