TechGist05

This is a reprint of a post published on the SDL website and closely related to a previous post on the difficulties and challenges of building Russian to English MT systems.  Many may not be aware that several in the SDL MT team have very significant research and development credentials, and have pioneered the data-driven approaches to MT system development. They initially commercialized Statistical MT and the members of the original research team read like a Who's Who in the MT research community. The original principals at Google MT and the developer of Moses both come from the original Language Weaver team and today you will see members of the original team have a hand in every major MT initiative in the US.  The SDL MT team also has the unique experience of working closely with linguists and translators on a long-term basis and thus have unique exposure to ongoing human MT quality assessments for most of the systems they build.  In localization use case scenarios MT...

When we consider the history of machine translation, the science by which computers automatically translate from one human language to another, we see that much of the science starts with Russian. One of the earliest mentions of automated translation involves Russian Peter Troyanskii who submitted a proposal that included both the bilingual dictionary and a method for dealing with grammatical roles between languages, based on the grammatical system of Esperanto , even before computers were available. The first set of proposals for computer-based machine translation was presented in 1949 by Warren Weaver , a researcher at the Rockefeller Foundation in his now famous " Translation memorandum ". In the famous memorandum referenced here, he said: “it is very tempting to say that a book written in Russian is simply a book written in English which was coded into the Russian code.” These proposals were based on information theory , successes in code-breaking during the Second Wo...

This is a modified and updated post that was originally published in CMS Wire on June 1st. We live in an era where there is more information available to a digitally savvy human than has ever been possible in the history of mankind as we know it. The volume growth implications are so significant and substantial that it is worth considering some contextual facts to get a proper understanding of this fact. The Encyclopedia Britannica announced in 2012 that after 244 years, dozens of editions, and more than 7M sets sold, no new editions would be printed. The 32 volumes of the 2010 installment, it turns out, were the last edition of this great publication. The primary cause for this was the increasing use and relevance of the Wikipedia (and other digital alternatives) which would cover 2,670+ Encyclopedia Britannica sized volumes if it were to be actually printed. While some may argue that the Wikipedia is less reliable, this contrast is quite astonishing and clearly, there is more inform...

Another guest post by Luigi who covers a variety of subjects here: AI, Big Data, NMT Hype, and more. Luigi attempts on a regular basis to clarify the conflation that seems rampant in the translation industry and makes my life easier by producing what I perceive as interesting content to keep this blog relevant and current.  AI is a much-misunderstood term and thus I think it is worth a closer look to further reduce the conflation that surrounds it.  The graphic below from a presentation I made on "Linguistic AI" on behalf of SDL, describes what I think a real AI should do. However, the reality is still quite far from the broad promise made by the use of the word intelligence, and most of what we see today is narrowly focused ML deployments that indeed do seem to perform some kind of cognitive function around carefully selected data.  There is also a lot of confusion about what machine learning (ML) is and how it relates to AI. Thus I think this graphic below is also usefu...