Tag: Translation Technology Development

Issue #27 – Use case: Neural MT for the Life Sciences

Issue #27 – Use case: Neural MT for the Life Sciences

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Neural MT has had quite a significant impact on how global enterprises are looking at translation automation to improve existing workflows. Above and beyond that, however, organizations are considering how machine translation can transform key areas of their business.

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Graduate Interviews: It Is Hypocritical to Assume a Translator’s Role is Sacrosanct

Graduate Interviews: It Is Hypocritical to Assume a Translator’s Role is Sacrosanct

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Last September, shortly after her graduation from the Johannes Gutenberg University of Mainz, Ms Ekaterini Ntouska joined Lionbridge Poland to work as a Linguistic Game Tester. Thanks to her flawless results in the Memsource Student Certification Program and her positive, friendly, and highly proactive approach, Ekaterini also became a Translation Intern at PureFluent through our Talent Endorsement Program.

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Did you lose hope in your past translations? Try again!

Did you lose hope in your past translations? Try again!

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Written by : Eman Ebehiry

After knowing more about the Translation memory, our assiduous translator starts lamenting his luck for not knowing it earlier. He looks at his work with pity for the loss of those past years of hard work, and his coming years of re-making what he already did.

But for one more time, technology saves the translator’s world, and offers him the solution: “The Alignment”.  What is translation alignment? How does it work? When would I use it? And why? What types of translation alignment are available? Are all question will be answered in the article.

First, translation alignment is the matching that we do between the source and its translation in the target in order to make a translation memory out of it. Simply it works through pairing the segments in its language pair; thus, it will be stored as units just like any translation memory. There are actually many ways of doing this. Furthermore, there are many tools that can get the job done, such as

A translator aligns his previously-translated material to build a database, instead of building one from scratch. It saves his time, and his previous work experience that might be repeated. But only the initial step of alignment that might take some time; as it may happen that the tool align some segments you have merged, or even split. So it’s closer to be giving probabilities. Hence, the human factor will be needed in reviewing and editing.

Now, what about trying to make your own alignment translation memory, and tell us about your experience?

You can learn to align your previous work from here via Trados.

Or if you have tried it already, share some of the benefits with us.

Why technology is not the key to translation business

Why technology is not the key to translation business

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This logic works for all businesses, including translation businesses. The most important asset of any company is not its race cars or its tools or its computers: it is the people, and the interactions between them. Technology is just a secondary, supplementary factor, a means of facilitating those interactions.

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memoQ Ranked as the Fastest Growing TMS on SaaS 1000

memoQ Ranked as the Fastest Growing TMS on SaaS 1000

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The memoQ Server was introduced 15 years ago to provide a comprehensive platform that significantly facilitates translation and related management tasks. Acquiring a perpetual license with an on-premise deployment was our main option until 2014, when we introduced memoQ Cloud, a new deployment method that brought a new licensing model – subscription (or SaaS). memoQ Cloud became our fastest selling product, growing over 80% in 2018 alone. We believe strongly in memoQ Cloud and have a very ambitious roadmap to make it even better. That is why we were delighted to see that memoQ has been named the fastest growing SaaS company in the global translation technology sector.

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Translation Automation with a Human Touch

Translation Automation with a Human Touch

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Automation is advancing quickly in the translation industry, too. Translation management systems are becoming comprehensive service platforms with numerous functionalities to help your company reach the highest level of efficiency possible. But although there are almost always brilliant technological solutions available for every single problem or action, a human touch can sometimes make the difference.

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Edit Distance: Not a Miracle Cure

Edit Distance: Not a Miracle Cure

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Edit distance metrics have also been suggested as a means to replace or support BLEU (bilingual evaluation understudy), which is an algorithm for evaluating the quality of text that has been machine-translated from one natural language to another and is used for assessing MT quality – thus support MT engine development. These discussions are not necessarily new; however, edit distances have not become mainstream as of yet. Why? The apparent ease of this solution does in fact hide a lot of complications. Let’s take a closer look.

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Transitioning to a post-editing machine translation business model

Transitioning to a post-editing machine translation business model

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When someone argues that MT engines produce poor results, the first thing I ask is when they last tested machine translation. Many in the industry are still basing their opinion on results from years ago, which are no longer valid. The reality is that machine translation is cheaper, faster, more secure, and increasingly better quality. LSPs that do not adopt this quickly dominating technology will not be able to compete in this new market.

Read more about machine translation post editing.

Here’s Why Neural Machine Translation is a Huge Leap Forward

Here’s Why Neural Machine Translation is a Huge Leap Forward

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When building rules-based machine translation systems, linguists and computer scientists joined forces to write thousands of rules for translating text from one language to another. This was good enough for monolingual reviewers to be able to get the general idea of important documents in an otherwise unmanageable body of content in a language they couldn’t read. But for the purposes of actually creating good translations, this approach has obvious flaws: it’s time-consuming and, naturally, results in low quality translations.

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