SMT is based on the comparison of parallel texts with high content volume and the calculation of the most probable translation. SMT has a feature of self-training, and translation quality with this approach depends directly on the volume of the parallel data for training. SMT engines today are mostly created on the basis of Moses, a free statistical machine translation engine.

Following the industry trend, PROMT researched Moses and developed its own approach for training an SMT engine on given parallel corpora.

Key Components:

Key Advantages:

As an SMT engine is trained on given texts, PROMT trains a client-specific SMT engine for a dedicated client. PROMT uses its own algorithms to correct the alignment in the client's corpora and to take into account the specifics of the target language.