Our Analysts' Insights

Every new development in the field of machine translation (MT) is accompanied by a set of numbers that purport to show dramatic improvement in terms of quality, usually BLEU or METEOR scores. These measures use a scale from 0 to 100 to quantify how similar the MT output is to one or more human translations of the same source text based on a mechanical analysis of how many of the same words show up and how likely they are to appear in the same order.

Is your company looking for ways to increase translator and interpreter efficiency, boost motivation, increase your teams’ output, and focus team members on billable work? Is simple compensation not enough? If so, maybe you need to look beyond tools, processes, and word rates to make work seem less serious by introducing something missing from translation memory tools: fun.

Recent advances in neural machine translation (NMT) represent a significant step forward in machine translation capabilities. Although most media coverage has significantly oversold the technology, one of Google’s announcements may actually be the most important one in the long run – the first successful deployment of zero-shot translation (ZST).

By now, most language professionals have seen that claims that neural machine translation (NMT) is delivering results as good – or almost as good – as human translation. If these claims – which have been repeated in the mainstream tech press without much examination – are accurate, it is only a matter of time before human translators will be out of work.

Does your site support Chinese, German, French, Spanish, or other major languages? What about Albanian, Arabic, Azeri, and Mongolian? Could you be missing out on opportunities for increased sales because you are targeting the wrong languages? Probably, but you aren’t alone.

Machine translation (MT) has caught the public eye once again. The Wall Street Journal recently predicted that “the language barrier is about to fall” – within 10 years. We might note that this is one barrier that has been “about to fall” for far longer than the actual Berlin Wall stayed up, and that it’s been an awfully long 10 years since the first such claims were made in the 1950s.

Better machine translation (MT) is surely at the top of many organizations’ globalization wishlists. But they all wonder how they can develop good MT engines that produce usable results.