Redefining Translation Quality: from User Data to Business Intelligence

Translation Quality eBook TAUSTranslation quality is one of the key concepts in the translation industry today. Measuring and tracking translation quality is essential for all players of the industry. More and more translation vendors offer different types and levels of quality resulting in dynamic pricing. Translation buyers are seeking to know whether their customized Machine Translation (MT) engine is improving and they would like to compare different MT providers. Finally, translators need to set the threshold of TM/MT matches at the most optimal levels. These are just a few examples where translation quality becomes central and increasingly tuned to user satisfaction.

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Japan’s translation industry is feeling very Olympic today

This article originally appeared in TAUS Review #3 in April 2015

Since the city of Tokyo was elected as the Host City of the Olympic Games in September 2013, Japan started undergoing a transformation in preparation of its leading role in the biggest international sporting event. If we could call the 64’ Olympic Games an opportunity for Tokyo to reshape its infrastructure and become Kenzo Tange’s visionary metropolis, then this decade marks the transformation to a global capital, the axis of international communication and multilingual dialogue.

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Everything you ever wanted to know about Google Translate, and finally got the chance to ask

This article originally appeared in TAUS Review #3 in April 2015

Google Translate is the world’s best-known free tool for machine translation. It is made possible by Google’s huge trove of data, and the statistical techniques that match n-grams from one language with plausible n-grams on another. For an outsider to the translation industry like me, Google Translate seemed to represent a great leap forward in translation quality when it was first introduced. However, since then, its quality improvements seem more incremental, when they are visible to all. How did Google Translate get so good? And how can it avoid plateauing in quality, and get better still?

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Language Data Faces the Philosophers

This article originally appeared in TAUS Review #3 in April 2015

Modern language technology is mostly based – one way or another – on “big data”. Each language has been around for a long time, and some of them over a very wide range of user communities. Modern digital techniques of storage mean that it is easy to bring together the record of a language’s past use, and look for patterns in it.

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Breakthroughs from Research #3

This article originally appeared in TAUS Review #3 in April 2015

In the last few years, Internet and data have been the engine for change, affecting global communications in every area, including the translation industry.

Big data and the IoT

A few weeks ago, at the International Consumer Electronics Show in Las Vegas, 2015 has been designated as the year of connected devices. From toothbrushes that can schedule check-ups with dentists to yoga mats that can analyze āsana in real-time, over collar-powered trackers helping owners locate their runaway pets.

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Data on Data

This article originally appeared in TAUS Review #3 in April 2015

I’m a historian by training, so I’m naturally interested in historical developments in the world of translation—especially in relation to translation technology and the employment of that technology. It’s a bit of a trend these days, but historians have always been eager to find data on which they can base their findings. That’s why I found it disappointing to realize that it’s not easy to come by data on the topic of this column: data. Particularly difficult to find: data on how translators use data.

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BabelNet – How the World Can Help Disambiguate Words

This article originally appeared in TAUS Review #3 in April 2015

In a landmark article well over a decade ago, the US psycholinguist George Miller (and initiator of WordNet), the well-known computational thesaurus of English) showed how a fairly banal couplet from the American poet Robert Frost’s poem Stopping by Woods on a Snowy Evening that goes:

But I have promises to keep 

And miles to go before I sleep

can cumulatively generate a total of 3,616,013,016,000 possible compound meanings if each individual word’s different dictionary meanings are broken out and aligned. This way semantic madness lies – at least for a computer. Luckily the outcome of a European project called BabelNet is now making it much easier to think through the classic problem of word ambiguity for the translation industry and others. Let’s look at how we got there and what BabelNet can offer by way of a solution.

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Preemptive Disambiguation

This article originally appeared in TAUS Review #3 in April 2015

As any professional translator knows, high quality translation depends on understanding the context of the source material. This article introduces the concept of preemptive disambiguation, along with an example microformat that can be embedded in online documents to make them easier to translate accurately.

The basic idea behind preemptive disambiguation or “Pre D” is to embed information in a document, in a format that is hidden from normal users and does not damage the visible document layout, but visible to any machine or professional translators who are working on it.

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Translation in Ethiopia – a long way to catch up with the industry as is practiced in the rest of the world

This article originally appeared in TAUS Review #3 in April 2015

In my previous two articles, I provided readers with a general glimpse of translation and localization contexts and practices in Africa. Those articles, I believe, have helped the readers understand where the industry is in Africa compared to where it is in other parts of the world.

In this third article, I try to picture the translation industry (I deliberately did not include localization here, because it isn’t yet to that stage) in Ethiopia. I write this article from my own experience and from a chat with two veteran translators. It will broadly cover the qualification and professionalism of translators, quality of translation and the nature/type of translation and the market as well as future prospects.

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The Circle of Data, Information, Knowledge, Understanding, and Wisdom

This article originally appeared in TAUS Review #3 in April 2015

Instead of being a comprehensive catalogue of translation-related data, this article will generalize data gathering and curating approaches, especially for Asian translation demands. Before going into details, let’s review the famous hierarchy of data, information, knowledge, understanding, and wisdom (DIKUW). We will then morph this into a circle, or a positive feedback loop, in hope of shedding some light on the path of pursuing bigger and smarter data.

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TAUS Post-Editing Guidelines

Machine translation (MT) with post-editing (PE) is fast becoming a standard practice in our industry. This means that organizations need to be able to easily identify, qualify, train and evaluate post-editors’ performances.

Today, there are many methodologies in use, resulting in a lack of cohesive standards as organizations take various approaches for evaluating performance. Some use final output quality evaluation or post-editor productivity as a standalone metric. Others analyze quality data such as “over-edit” or “under-edit” of the post-editor’s effort or evaluate the percentage of MT suggestions used versus MT suggestions that are discarded in the final output.

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One Product, One Quality (But Make Sure it’s Not Perfect)

Quality means more than just linguistic quality. This is an undisputed statement by now. As a startup, you have a lot to think and worry about while developing your product and your primary focus should be the user – especially the paying one. You want your user to be satisfied with the product’s usability, the whole user experience (UX), but also with its localization. The tricky part is that you generally cannot ask your users to evaluate the localization of the product without evaluating the product at the same time. And there cannot be real science about quality.

For evaluation, you can rely on experts with a vested interest in quality. You can just focus on higher tier languages or you can simply try and get an average out of the range of different opinions and understandings. As Richard Sikes puts it, there are situations where we tend to work using a “reactive” approach to quality, instead of a “proactive” one and as a consequence the quality plan we devise and implement is also of a reactive kind.

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