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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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In the past few years, the translation industry has burst with new tools, platforms, and solutions. In fact, almost every week we hear of new tools or solutions that promise to improve translations in many ways. We’ve seen that translation and, in particular, translation technology have become a very interesting and dynamic sector for innovators from both inside and outside the industry. Of course, there is a strong demand for translations of any kind. Globalization of trade, social media, news and politics expose not only businesses but also consumers, governments, and NGOs to audiences speaking in many different tongues and...
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During the TAUS Annual Conference 2016 in October, panelists will discuss "How to deliver high-quality translations for long-tail languages". This blog post is written in preparation for this session and as background for our pre-conference survey (see link below).    CLICK HERE TO FILL IN THE SURVEY   When Nelson Mandela was asked ‘why he spoke the politically charged language Afrikaans with the South African Apartheid government during negotiations’, he answered: “If you talk to a man in the language he understands, that goes to his head. If you talk to him in his language, that goes to his heart.” Mandela’s words well...
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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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