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.
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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.
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.