How to Move to Neural Machine Translation for Enterprise-Scale Programs—An Early Adoption Case Study

While Neural Machine Translation (NMT) technology has been around for a few years now in research and development, it is still in its infancy when it comes to customization readiness and experience with implementation on an enterprise scale with Language Service Providers (LSPs). For large, multilanguage LSPs, it is therefore not only important to stay up-to-date on latest research on the technology as such, the best use cases, as well as main advantages and disadvantages. Moreover, due to this infancy, the challenges encountered during an early adoption of the technology in an enterprise-scale translation program are of a very practical and concrete nature and range from the quality of the NMT output over availability of language pairs in (customizable) NMT systems to additional translation workflow investments and considerations with regard to involving the supply chain. In an attempt to outline the above challenges and possible approaches to overcome them, this paper describes the migration of an established enterprise-scale machine translation program of 28 language pairs with post-editing from a Statistical Machine Translation (SMT) setup to NMT