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This article is the first in a series we'll be posting, exploring the topic of machine translation. Powering the SmartCAT ecosystem with flexible third-party integration capabilities, we’re preparing to enter the Convergence era, in line with the insightful outlook* Jaap van der Meer presented on TAUS in 2013. Not just evolve with the industry but take the lead in this process, that’s what we’re up to. *Read Jaap’s article on TAUS here.   In November last year, Google and Microsoft announced they have updated their translation engines with Neural Machine Translation (NMT) systems. Both companies celebrated a significant boost in the quality...
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Jean Gachot, the major business developer in the career of Systran, has died. Jean Gachot, who acquired Systran and widely developed their machine translation system, passed away on February 17 at the age of 96, missing the company’s 50th anniversary by just one year. The remarkable history of Systran can be attributed to a large extent to Mr. Gachot, who developed a passion for machine translation in the 80s of the last century. At this time he met Peter Toma, the legendary inventor of one of the first tested and demonstrated commercial MT engines, and, in 1985, struck a deal that...
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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. Although the translations of general-purpose MT systems (as opposed to more specialized, domain-specific ones) may not always be of good quality (especially when morphologically rich languages or ideographic ones are involved), users still have the opportunity to get (for free) the gist of a text or a web page written in...
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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. In general terms, DL is a family of machine learning algorithms that use multilayer artificial neural networks to efficiently learn representation of high-level features from noisy...
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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. However, creating machines that can adequately replicate the immensely complicated and nuanced translation styles of a professional translator is still a far cry in the field of translation technology. At KantanLabs, we are researching advanced Hybrid MT systems, which will incorporate the best of both worlds – the tried and tested traditional Statistical...
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Guest - Aveitos
Way cool! Some extremely valid points! I appreciate you penning this article and also the rest of th...
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I am greatly impressed to know about your professional activities especially in the ...