"Specializing" Neural Machine Translation in SYSTRAN
We see that Neural MT continues to build momentum and that already most people agree that generic NMT engines outperform generic phrase-based SMT engines. In fact, in recent DFKI research analysis, generic NMT even outperforms many domain-tuned Phrase-based SMT systems. Both Google and Microsoft now have an NMT foundation for many of their most actively used MT languages. However, in the professional use of MT where the MT engines are very carefully tuned and modified for a specific business purpose, PB-SMT is still the preferred model for now. It has taken many years, but today many practitioners understand the SMT technology better, and some even know how to use the various control levers available to tune an MT engine for their needs. Today, most customized PB-SMT systems involve building a series of models in addition to the basic translation memory derived translation model, to address various aspects of the automated translation process. Thus, some may also add a language model t...