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.
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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.
It was Renato (Beninatto) who reminded me, in the ‘Future’ panel discussion in Dublin, that only eleven years ago (when the TAUS think tank was founded) nobody - in his right mind - would think about using machine translation (MT) technology on any job anywhere. And now? Now MT is everywhere. Insiders say that everyday computers translate 200 Billion words. That is 100 times more than the output of all human translators together. MT is everywhere and always there, except … well, except the professionals seem to have their doubts. That makes me think that the state of the industry could be better.
When I attended translation courses, I was assigned to write a commentary on George F. Will’s column Reading, Writing and Rationality on the Newsweek issue ofMarch 17, 1986.
Even then, with no Internet and television as the dominant media, students were urged to read.
Content creation and localization as a challenge
Startups are organizations designed to search for a “Big Idea” and to monetize it. They constantly reinvent themselves and explore innovative business models that disrupt existing markets. They learn by trial and error. Incremental growth is of paramount importance to them and speed is essential to beat the competition and to establish their businesses.
Since its launch in 2007, Google Translate has brought machine translation to the masses, making it free, quick and easy, and therefore significantly contributing to the demolition of the language barriers.
The term ‘creative content’ is used a lot but what does it really mean? And, as a global company, how do you handle creative content translation? How does the translation process differ from regular translation? What type of translator do you use? How does the terminology differ? Is the creative content industry specific? Let's explore these questions in more detail.
Within translation/localization we’re all looking to ensure that the quality of the deliverable meets the expectation, in whatever way those two things are defined. Some people may believe achieving that quality is a factor that is inherent in actually doing the work, and builds around the processes. Other people may, however, think of quality as something that can be tested and evaluated after the work is done.
Globalization and new technology have not left the translation services of the EU institutions untouched. Successive enlargements have more than doubled not only the number of member states but also the number of official languages. The extension and deepening of the European integration have led to more and more different text types being produced and to an ever-increasing translation demand. At the same time, in-house translation staff per language is progressively being reduced. The pressure to provide more, faster and with fewer resources has led to a need to constantly re-think how we carry out the translation work. We all know the buzz words: doing more with less, doing more and better with less, being effective and efficient, doing the right things and doing them right. Increased focus on processes and on making optimal use of technology does lead to efficiency gains. Mostly, however, what saves the situation is an increased use of outsourcing, which implies challenges for the quality assurance.