TechGist05

While many people may consider that all post-editing is the same, there are definitely variations that are worth a closer look. This is a guest post by Mats Dannewitz Linder that digs into three very specific PEMT scenarios that a translator might view quite differently. Mats has a more translator-specific perspective and as the author of the Trados Studio manual, I think provides a greater sensitivity to the issues that do matter to translators.  From my perspective as a technology guy, this post is quite enlightening as it provides real substance and insight on why there have been communication difficulties between MT developers and translator editors. PEMT can be quite a range of different editor experiences as Mats describes here, and if we now factor in the change that Adaptive MT can have, we now have even more variations on the final PEMT user experience.   I think a case can be made for both major cases of PEMT that I see from my vantage post, the batch chunk mode and the inter

At the outset of this year, I felt that Adaptive MT technology would rapidly establish itself as a superior implementation of MT technology for professional and translator use. Especially, in those scenarios where extensive post-editing is a serious requirement. However, it has been somewhat overshadowed by all the marketing buzz and hype that floats around Neural MT's actual capabilities. Had I been a translator, I would have at least experimented with Adaptive MT, even if I were not to use it every day. If one does the same type of translation (focused domain) work on a regular basis, I think the benefits are probably much greater. Jost Zetzsche has also written favorably about his experiences with Adaptive MT in his newsletter. We have two very viable and usable Adaptive MT solutions available in the market that I have previously written about: Lilt :- An Interactive & Adaptive MT Based Translator Assistant or CAT Tool and A Closer Look at SDL's Adaptive MT Technology  

This is a guest post by Roberto Navigli of BabelNet, a relatively new "big language data" initiative that is currently a lexical-semantic research and analysis tool, that can do disambiguation and has been characterized by several experts as a next-generation dictionary. It is a tool where "concepts" are linked to the words used to express them. BabelNet can also function as a semantics-savvy, disambiguation capable MT tool. The use possibilities are still being explored and could expand as grammar related big data is linked to this foundation. As Roberto says:"We are using the income from our current customers to enrich BabelNet with new lexical-semantic coverage, including translations and definitions. In terms of algorithms, the next step is multilingual semantic parsing, which means moving from associating meanings with words or multiword expressions to associating meanings with entire sentences in arbitrary languages. This new step is currently funded by t

 This is a guest post on Translation Metadata by Luigi Muzii. Some may recall his previous post: The Obscure and Controversial Importance of Metadata . Luigi's view of translation metadata is much broader and all-encompassing than most descriptions we see in the translation industry which usually only reference TM descriptors . In addition to descriptors about the TM, it can also be about the various kinds of projects, the kinds of TM, translators used, higher levels of an ontological organization, client feedback, profitability and other parameters that are crucial to developing meaningful performance indicators (KPI). As we head into the world of AI-driven efficiencies, the quality of the data and the quality and sophistication of the management of your data becomes significantly more strategic and important. I have observed over the years that LSPs struggle to gather data for MT engine training and that for many if not most, the data sits in an unstructured and unorganized ma