One of the major advantages of neural machine translation (NMT) is that unlike statistical machine translation (SMT), which was the previous industry standard (and is still actively used in commercial applications), NMT is trained and used jointly as a single end-to-end system without the need to optimize multiple independent models and relations between the models.
training NMT systems for individual language pairs has shown to take significantly more time (e.g., two to three weeks or up to a week with newer platforms, such as Marian (Junczys-Dowmunt et al., 2016) or Google's Tensor2Tensor toolkit 1 ) than training of SMT systems (e.g., less than a day or up to several days for large systems).