Datafication of Translation

The translation industry has come to a new era of datafication. This is a trend no one can stop and it also means human beings are much more thoughtful and considerate about their past work and legacy data. By continuously feeding the machine with data and interacting with machine through post-editing and manual data adjustment and annotation, human beings are producing smarter machine-operated systems. This includes Machine Translation (MT) engines, terminology management platforms, translation memory tools, translation management software, and multilingual annotation systems.

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Treating Translation Quality Metrics as Business Intelligence

Moderated by Clove Lych and Eduardo D’Antonio (VMWare)

At the 2015 TAUS Annual Conference, panelists Steve Richardson (LDS Church), Samuel Läubli (Autodesk), Hyunjoo Han (Autodesk), and Nancy Anderson (EMC) discussed the methods of implementation and results obtained from translation quality measurements used at their organizations. The panel ended with a consensus about the advantage of sharing selective, anonymized data within similar industries.

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QE Summit, San Jose 2015 – Benchmarking MT Engines

The session on MT benchmarking was a follow-up from last year’s QE summit where participants at a breakout session (including the session leader John Paul Barraza from Systran) expressed the need for transparency of MT evaluation metrics. Discussants agreed: one of the main problems in the translation industry today is the lack of benchmarking. The output of MT engines cannot be compared to industry averages or standards because these are not yet available.

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Dynamic Translation Quality in the Business World

The concept of quality is relational. At the center of the judgment, people are evaluating the relationship between the product and its reference criteria. The earliest benchmarks for translation quality are two types of text(s), or linguistic data. One is the source text and the other is non-translated texts in the target language. In the former case, quality evaluators compare the translation product against its source text in order to determine its faithfulness, or adequacy level as stated in the TAUS Dynamic Quality Framework (DQF) Adequacy/fluency guidelines; and in the latter, evaluators compare the translation against non-translated texts in the target language in order to judge the naturalness of the translation, or in TAUS’s term, the “fluency” of the translation.

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QE Summit, San Jose 2015 – Quality Evaluation of Non-Conventional Content

In the session entitled Quality evaluation of non-conventional content, Sonia Oliveira (Zynga) discussed with her panelists the challenges of localizing and evaluating new content types. There is a pressing need to ensure quality, even for non-conventional content such as games, travel reviews and movies. One of the aims of the session was to define what’s relevant to QE for content that might have a short lifecycle within the product versus other aspects of the player or user experience that might be more critical.

We examined the role of traditional linguistic and functional quality testing vis-à-vis new approaches which focus on areas that users might consider more important. How to balance quality — as we currently define it — with real expectations set by users, keeping in mind that user expectations might touch a variety of product areas and not include linguistic quality at all?

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Dynamic Quality and Datafication

As the needs of content producers have evolved, the need for a more flexible understanding of what constitutes quality has become more apparent. Take for example an agile software development project, with localization drops spread throughout the production cycle. The most relevant set of parameters to measure might be translation adequacy, technical functionality (no errors in the code), and on-time delivery (to support the agile development cycle). On the other hand, consider the example of a creative text accompanying a marketing campaign. The most relevant set of parameters here would likely be different from those of the software project. They might include suitability for the target locale, fluency, style, or creativity – all of these to maximize the impact of the message on the target.

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QE Summit, San Jose 2015 – Business Intelligence

Business IntelligenceTranslation and localization is a data rich industry – translation memory, time to translate, edit distances, and quality evaluation metrics. How can we leverage this rich seam of data to help make better decisions in our localization projects?

Referring back to Business Intelligence (BI) patterns and usage from other industries, the speakers looked at how these patterns could be applied to translation and localization. Tom Shaw (Capita), the leader of this session started off emphasizing that BI data should in the first place offer real solutions, that is, how to target customers and offer them real benefits and how to drive a better service.

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The Proof of the Pudding

This article originally appeared in TAUS Review #2 in January 2015

"The proof of the pudding is in the eating." This old saying dates back to the 17th century and is widely attributed to the Spanish author Cervantes in his world famous novel “The Ingenious Gentleman Don Quixote”. It can be paraphrased with You can only say something is a success after it has been tried out. Applying it to translations you could say: the test of a translation is in its use. The question here is: who is eating the pudding i.e. who is using the translation and for which purpose? And what taste do they have?

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Translation in Africa

This article originally appeared in TAUS Review #2 in January 2015

Many Africans speak at least 2, often many more different languages. You would expect that language is a big thing, but for many people it seems to be a non-issue. The Yellow Pages of Kenya (pop. 44 million, over 60 languages) has just 7 entries under ‘Translations’. Of all 400 GALA members there are a handful in Egypt and just 2 in South Africa; no GALA members in any of the other 51 African countries. 

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Mission Possible

This article originally appeared in TAUS Review #2 in January 2015

The Translator's PerspectiveI'm a man on a mission these days. Sort of. Here's my mission: As I've said in the previous column -- it's in all our interests to find better ways of utilizing machine translation than we have so far with post-editing. And "all" really means all, including translation professionals and translation buyers. There is a lot of potential in harvesting data from machine translation suggestions, but overall I think we're going about it the wrong way.

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Breakthroughs from Research #2

This article originally appeared in TAUS Review #2 in January 2015

In the first issue of this journal, I wrote that we are in the exponential side of the growth curve, the second half of the chessboard, to quote Ray Kurzweil, where every change has a significant impact.

In the last few weeks, an article caught my attention about how wearable devices are about to radically reshape the customer service experience in the banking industry.

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Fire and Water

This article originally appeared in TAUS Review #2 in January 2015

The linguist's perspectiveLast quarter I looked at two kinds of blocks to language learning, each of which can be reversed with powerful effect. 

One was the cognitive struggle to learn words and patterns, all of them meaningless at first. “How do I learn this stuff? How can I get enough of it to be ready when somebody actually uses the language to me?”

The other was affective and emotional. “How can I cope when I fail to understand? What to do about the humiliation, when I can’t even keep up with little children?” 

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