We are happy to announce that a new Globalese plugin is available in Memsource. This plugin provides a way for Globalese Cloud users to look up individual sentences or pre-translate entire files in Memsource by using the Globalese Text translation service. For more information, please refer to the related product documentation page.
We can confirm that we are not using the Log4j Java code library in our self-developed Globalese code, services, and environment.
We are checking continuously if there is any impact from 3rd party products/services. We are following the events around the CVE-2021-44228 bug, and will take measurements if any new solution, patch, or fix will be available.
Globalese MT is very happy to welcome t’works in the Globalese user community. t’works is a one stop translation service provider specialising in highly individual, complex projects and is based in 13 locations across 6 European countries. Globalese is excited to work with t’works and to help offer t’works’ clients the increased productivity benefits that machine translation brings. We’re looking forward to building a long and successful relationship.
The Globalese team led by CEO Gábor Bessenyei is looking forward to meeting you at the GALA's annual Language of Business conference in sunny Munich.
Neural Machine Translation was an amazing break-through from many points of view. It has improved the overall quality of machine translations compared to pre-neural times. It has provided, for the first time, truly usable and sound quality output for the language industry. It has also opened up opportunities for languages like Japanese, Chinese or Russian, which otherwise performed poorly on Statistical MT technology.
The downside of the Neural Machine Translation revolution: terminologyAs with every groundbreaking invention, NMT technology also had its limitations. One of the major issues with Neural was handling terminology. This major challenge stems from the very reason of what makes NMT so truly exciting. Unlike with statistical MT technology, where it was possible for users to provide a terminology list, which the MT system could safely rely on during translation, it was not directly possible to provide a master terminology for the translation process in the NMT world. Technically, you can, of course, introduce a glossary to an engine as part of the training corpora, but this will not act the way you would expect. It will not prioritize the translations in the glossary over the content in the rest of the training data. In the NMT technology, there is currently no way to influence the terminology translation directly during the machine translation process.
Are you a content owner or an LSP? Give Globalese a go now and grow your business with the power of Neural MT! Click here and start your free trial now!That doesn’t mean that developers hasn’t made attempts to solve this issue. One of the solutions we have seen from many MT providers is to implement terminology replacement based on a glossary after the machine translation phase. While it certainly sounds promising, unfortunately the results are not always that encouraging. The problem is that you are running a considerable risk of losing grammatical information during the replacement process. Just imagine the problems a changed gender of a word can cause in German. In better cases, you will have to spend many hours of editing to fish out the problematic bits. In some cases, you end up with a limited usability output that leaves you, your clients and your translators disappointed.