On 11 November, Daimler hosted an Automotive Translation Roundtable organized by TAUS and berns language consulting. Translation managers from eight large automotive and three large IT companies participated in the one day meeting. Goals for the day were to get the pulse of the translation sector and learn from each other. What do we have in common? Where do we differ? It comes down to this: we are not so different. And what’s more: we must work together across the translation sector to create a common ecosystem.
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What makes a good conference? If you ask me, the answer is: purpose, people and program. As simple as that. Let’s start with ‘purpose’: you have to have a good reason to make people travel from all over the world to a single location and have them spend a few days of their precious time together. As Eric Liu, General Manager of Alibaba Language Services, said in his keynote at the TAUS Annual Conference in Portland last week: it all starts with a mission - “Preparing for a future that is without language barriers”. The same goes for TAUS and the TAUS Annual Conference. Why do we have a conference - what is the purpose? Because we want to work together to help the world communicate better.
The amount of time and money you spend on quality management easily constitutes 20% of the total translation time and costs. A large part of this percentage consists of translation review (or quality review). You can reduce translation review time by streamlining the review process. In this post, we’ve listed 5 ways to do this.
During the TAUS Annual Conference 2016 in October, Eric Bailey, Group Engineering Manager for the Global Service and Experiences team within Office at Microsoft, will host the session 'To Share Or Not To Share'? This blog post is written in preparation for this session.
Nowadays, in one way or another, machine translation (MT) is part of our everyday lives. Most likely Google made that happen, about a decade ago, by launching Google Translate, a free instant online general-purpose translator allowing users to translate any text (words, phrases, documents, web pages) in different language directions.
The last significant breakthrough in the technology of statistical machine translation (SMT) was in 2005. That year, David Chiang published his famous paper on hierarchical translation models that allowed to significantly improve the quality of statistical MT between distant languages. Nowadays we are standing on the verge of an even more exciting moment in MT history: deep learning (DL) is taking MT towards much higher accuracy and finally brings human-like semantics to the translation process.
Neural Machine Translation (NMT) systems have achieved impressive results in many Machine Translation (MT) tasks in the past couple of years. This is mainly due to the fact that Neural Networks can solve non-linear functions, making NMT perfect for mimicking the linguistic rules followed by the human brain.
Data entered the field of machine translation in the late eighties and early nineties when researchers at IBM’s Thomas J. Watson Research Center reported successes with their statistical approach to machine translation.
Until that time machine translation worked more or less the same way as human translators with grammars, dictionaries and transfer rules as the main tools. The syntactic and rule-based Machine Translation (MT) engines appealed much more to the imagination of linguistically trained translators, while the new pure data-driven MT engines with probabilistic models turned translation technology more into an alien threat for many translators. Not only because the quality of the output improved as more data were fed into the engines, but also because they could not reproduce or even conceive what really happened inside these machines.