![]() Another tool, Lilt, uses a system that updates the MT engine with every finished segment and interactively changes the MT suggestion with every word you enter.Kevin now works for SDL, and his technology will surely see the light of day in various SDL products.) (Freelance translator Kevin Flanagan developed Lift as his PhD project at Swansea University. Lift uses MT to identify subsegment matches in TMs so that even a TM with very little content can produce valid subsegment suggestions.A similar process is currently being developed for OmegaT. ![]() Star Transit uses a process called “TM-validated MT,” in which the communication goes the other way: content in the TM is used to evaluate MT suggestions.Déjà Vu uses MT fragments to “repair” fuzzy translation memory (TM) matches.In some cases, such as with Wordfast and Déjà Vu, these even come from a number of different MT engines. A number of tools, including Wordfast Classic and Anywhere, Trados Studio, Déjà Vu, and CafeTran, use auto-suggest features that propose subsegments of MT suggestions (which invariably are more helpful than the whole segment).Recognizing both this situation and the existence of valuable data, even in publicly available general MT engines, translation environment tool vendors looked at ways to bring that data into the workflow (aside from just displaying full-segment suggestions from MT systems that often aren’t particularly helpful). Why? Well, it’s a process for which the typical translator wasn’t trained, and it generally doesn’t match the expectation that translators bring to their jobs. True, there are still the “traditional” post-editors who work primarily on raw MT, but as any translation vendor who has tried to hire one can tell you, they’re hard to find. There are some exceptions, such as the Microsoft knowledgebase, but even that is post-edited, albeit with the P3 (post-published post-editing) process, a form of end-user post-editing that is strongly advocated by Chris Wendt, who leads Microsoft’s program management team for MT development.Īlthough it might have gone almost unnoticed in the “MT camp,” professional translators’ real use of MT is integrated increasingly into existing processes. No matter how good MT output might be, it cannot be trusted for publication-ready quality without a human post-editor evaluating the accuracy and correcting the translation. Machine translation (MT) and post-editing are inextricably connected for many within the translation world.
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