Machine Translation: the Right Expectations, the Right Priorities

Machine Translation today is a real productivity service. While the conclusion has been obvious looking at the performance data MT services deliver, many organizations with the right characteristics have now decided to adopt the technology to support their workflows, cut costs and save resources.

Will the rollout of a Neural Machine Translation solution lead you into the space age? Oh, well […]

Live Webinar: Deploying Neural Machine Translation in the CIS

Neural Machine Translation has changed the landscape for many languages and regions. In the era of Statistical and Rule-based Machine Translation, output for many languages spoken in CIS countries were of a very moderate quality. The application of MT for these languages remained rather theoretical until the rollout of the more robust Neural Machine Translation technology.

In this live webinar hosted […]

Breaking the terminology barrier in Neural Machine Translation


Neural Machine Translation was an amazing break-through from many points of view. It has improved the overall quality of machine translations compared to pre-neural times. It has provided, for the first time, truly usable and sound quality output for the language industry.  It has also opened up opportunities for languages like Japanese, Chinese or Russian, which otherwise performed poorly on […]

Stock corpora for training Machine Translation engines

Since the introduction of core and auxiliary corpora in version 3.1, we have seen successful and less successful MT engines trained in Globalese. The successful ones usually have ample and well-maintained core corpora (which we have renamed to ‘master’ in version 3.5 to resonate more with CAT tool users), have plenty of auxiliary corpora to use as the foundation, and […]

Augmented in-domain engines

In the past, LSPs and content owners with a need for MT would often struggle when building engines, because they wouldn’t have the required volumes of specific corpora to train successful engines. To tackle this, Globalese 3.1 introduces the concept of core and auxiliary corpora.

The small corpus struggle
To train a working MT engine, a training corpus of less than 100,000 […]

Composite engines in Globalese 2

Not all language pairs are created equal. Anyone who has experience with Statistical Machine Translation (SMT) knows it is always easier to get good results from an English to Spanish engine than say, French to Japanese.

The concept of composite engines makes its debut in Globalese 2.0. Every Globalese engine now includes a phrase-based and a hierarchical part. These reflect two […]

MT in the translation ecosystem

You justify your investment by the return on costs. However, on the long run, your savings should come from streamlining and not from pressurizing the members of your ecosystem who depend on you.

Straining the members of your organization may work for a while, but in the long run you will lose. You will lose your best translators, editors, proofreaders and […]

About MT Engine Quality

Machine Translation (MT) is becoming more and more part of the standard translation workflow. However, to use MT as a productivity tool for increasing the profitability of projects and decreasing delivery time, it is essential to utilize high-quality MT engines in projects. This post summarizes the most important points about the influencers on MT engine quality, focusing on Statistical Machine […]