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Archive by author: Arle LommelReturn

Senior Analyst

Focuses on language technology, artificial intelligence, translation quality, and overall economic factors impacting globalization

Translation Quality Tools Heat Up: Three New Entrants Hope to Disrupt the Industry

For several years, the field of quality checking tools has been largely stagnant, with incremental updates to established tools. Recently, TAUS’ Dynamic Quality Framework (DQF) and the EU’s Multidimensional Quality Metrics (MQM) have set the stage for new developments in quality assessment methods thanks to their new methods and push for standardization. In this blog, we’ll review three new market entrants that are hoping to shake up this area.
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A Cynic's Guide to "Winning" at Machine Translation

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.
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Game on! Motivate Translators and Interpreters with Gamification

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.
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Zero-Shot Translation is Both More and Less Important than you think

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).
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Is Neural MT Really as Good as Human Translation?

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.
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Underserved Lanaguages Create Challenges and Opportunities for Global Enterprises

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.
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MT is Changing the Industry, Just Not in the Way Mainstream Media Thinks It Will

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.
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The Path to Statistical MT Success is Paved with Big Data - and Not just Yours

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.
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