The language services industry offers an intimidating array of machine translation options. To help you separate the truly innovative from the middle-dwellers, your pals here at Lilt set out to provide reproducible and unbiased evaluations of these options using public data sets and a rigorous methodology. This evaluation is intended to assess machine translation not only in terms of baseline translation quality, but also regarding the quality of domain adapted systems where available. Domain adaptation and neural networks are the two most exciting recent developments in commercially available machine translation. We evaluate the relative impact of both of these technologies for the following commercial systems:
Translation is a complex art form – one we highly respect. The manual process involved makes it difficult to keep up with changing technology and progressive business practices. Modernizing seems formidable. Change breeds uncertainty. It’s understandable for localization teams to stick with legacy vendors, a static stack, and the same workflows.