But they all have one limitation in common: the underlying MT is static. Translators and their community have warmly welcomed these developments (though larger language service providers have taken less note because - at least so far - there really is no process in place that allows for measurements and monetization). In fact, there are too many other creative and productive uses of MT beyond post-editing to list them all here. Lift, Kevin Flanagan’s PhD project at Swansea University, used MT to identify subsegment matches in TMs so that even a TM with very little content can produce valid subsegment suggestion (Flanagan now works for SDL, and his technology will surely see the light of day in various SDL products). A similar process is being developed for OmegaT right now as well. Star Transit uses a process called “TM-validated MT” in which the communication goes the other way: Content in the translation memory is used to evaluate MT suggestions. In some cases, such as with Wordfast and Déjà Vu, these even come from a number of different MT engines.ĭéjà Vu uses MT fragments to “repair” fuzzy translation memory (TM) matches. 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 machine translation systems that often aren’t particularly helpful).Ī number of tools, including Wordfast Classic and Anywhere, Trados Studio, Déjà Vu and CafeTran, use auto-suggest features that propose subsegments of machine-translated suggestions (which invariably are more helpful than the whole segment). Why? Well, it’s a process that the typical translator wasn’t trained for, and it generally doesn’t match the expectation that translators bring to their job. 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.
#WORDFAST CLASSIC RECOVERY SAVE PROFESSIONAL#
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 Microsoft’s Chris Wendt.Īlthough it might have gone almost unnoticed in the MT camp, professional translators’ real use of MT is increasingly integrated into existing processes. No matter how good an MT output might be, it cannot be trusted for publication-ready quality without having a human post-editor evaluate the accuracy and correct the translation. For many in the translation world, machine translation (MT) and post-editing are inextricably connected. How do I know? I watched how folks responded to it at the recent ATA conference when Lilt was first unveiled to the public, and they were giddy. But, firstly, who is ever truly unbiased? And, secondly, I would be just as giddy about the prospects this tool holds if I were seeing it for the first time today. In fact, in the last few weeks before he and his team launched their product, I helped them make the tool fit more naturally into the process of the translator. You can choose to add another script specifying those and invoke it from "customscript.sh" with pre and post-options.Let me start with a disclaimer: I’ve known Spence Green, the creator of Lilt, for a little while now.
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#WORDFAST CLASSIC RECOVERY SAVE INSTALL#
Default is "/usr/local/ASR"Ĭhange directory to "VX/scripts" under install locationĬreate a bash shell script named "customscript.sh" with execute permissions for root user.Ī.
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Below are the steps to enable it.Ĭhange directory to Azure Site Recovery Mobility Agent install location. The custom script with pre and post-options will be used by the Azure Site Recovery Mobility Agent during app-consistency. Azure Site Recovery for Linux Operation System supports application custom scripts for app-consistency.