TechGist05

This is largely a guest post by Manuel Herranz of Pangeanic, slightly abbreviated and edited from the original, to make it more informational and less promotional. Last year we saw FaceBook announce that they were going to shift all their MT infrastructure to a Neural MT foundation as rapidly as possible, this was later followed by NMT announcements from SYSTRAN, Google, and Microsoft. In the months since we have seen that many MT technology vendors have also jumped onto the NMT wagon. Some with more conviction than others. The view for those who can go right into the black box and modify things (SDL, MSFT, GOOG, FB and possibly SYSTRAN) is, I suspect, quite different from those who use open source components and have to perform a "workaround" on the output of these black box components. Basically, I see there are two clear camps amongst MT vendors:  Those who are shifting to NMT as quickly as possible (e.g. SYSTRAN) Those who are being much more selective and either "go

This is a post by Vassilis Korkas on the quality assurance and quality checking processes being used in the professional translation industry . (I still find it really hard to say localization, since that term is really ambiguous to me, as I spent many years trying to figure out how to deliver properly localized sound through digital audio platforms . To me, localized sound = cellos from the right and violins from the left of the sound stage. I  have a strong preference for instruments to stay in place on the sound stage for the duration of the piece.  )   As the volumes of translated content increase, the need for automated production lines also grows. The industry is still laden with products that don't play well with each other, and buyers should insist that vendors of the various tools that they use enable and allow easy transport and downstream processing of any translation related content. Froom my perspective automation in the industry is also very limited, and t

This is a follow-up post by Luigi Muzii on the evolving future of the "professional translation industry". His last post has already attracted a lot of attention based on Google traffic rankings. In my view, Luigi provides great value to the discussion on "the industry" with his high-level criticism of dubious industry practices, since much of what he points to is clearly observable fact. Bullshit marketing speak is a general problem across industries, but Luigi hones in on some of the terms that are thrown around at localization conferences and in the industry. You may choose to disagree with him, or perhaps possibly see that there are good reasons to start a new, more substantive discussion. Among other things, Luigi challenges the improper usage of the term "agile" in the localization world in this post. The concept of agile comes from the software development world and refers most often, to the rapid prototyping, testing and production implementation

SDL recently announced some new initiatives in their machine translation product SDL Enterprise Translation Server (ETS). As with most press releases, there is very little specific detail in the release itself, and my initial impression was that there really was not much news here other than the mention that they are also doing Neural MT. Thus, I approached my SDL contacts and asked if they would be willing to share some more information with me for this blog, for the reader base who were curious about what this announcement really means. I initially set out with questions that were very focused on NMT, but the more I learned about SDL ETS, the more I felt that it was worth more attention. The SDL team were very forthcoming, and shared interesting material with me, thus allowing me to provide a clearer picture in this post of the substance behind this release , (which I think Enterprise Buyers, in particular, should take note of), and which I have summarized below. The press release