Category: Learning Zone

A Beginner’s Guide to Machine Translation

A Beginner’s Guide to Machine Translation

What is Machine Translation?

Machine translation (MT) is automated translation by computer software. MT can be used to translate entire texts without any human input, or can be used alongside human translators. The concept of MT started gaining traction in the early 50s, and has come a long way since. Many used to consider MT an inadequate alternative to human translators, but as the technology has advanced, more and more companies are turning to MT to aid human translators and optimize the localization process.

How Does Machine Translation Work?

Well, that depends on the type of machine translation engine. There are several different kinds of MT software which work in different ways. We will introduce Rule-based, Statistical, and Neural.

Rule-based machine translation (RBMT) is the forefather of MT software. It is based on sets of grammatical and syntactical rules and phraseology of a language. RBMT links the structure of the source segment to the target segment, producing a result based on analysis of the rules of the source and target languages. The rules are developed by linguists and users can add terminology to override the MT and improve the translation quality.

Statistical MT (SMT) started in the age of big data and uses large amounts of existing translated texts and statistical models and algorithms to generate translations. This system relies heavily on available multilingual corpora and an average of two millions words are needed to train the engine for a specific domain – which can be time and resource intensive. When a using domain specific data, SMT can produce good quality translations, especially in the technical, medical, and financial field.

Neural MT (NMT) is a new approach which is built on deep neural networks. There are a variety of network architectures used in NMT but typically, the network can be divided into two components: an encoder which reads the input sentence and generates a representation suitable for translation, and a decoder which generates the actual translation. Words and even whole sentences are represented as vectors of real numbers in NMT. Compared to the previous generation of MT, NMT generates outputs which tend to be more fluent and grammatically accurate. Overall, NMT is a major step in MT quality. However, NMT may slightly lack behind previous approaches when it comes to translating rare words and terminology. Long and/or complex sentences are still an issue even for state-of-the-art NMT systems.

The Pros and Cons of Machine Translation

So now you have a brief understanding of MT – but what does it mean for your translation workflow? How does it benefit you?

  • MT is incredibly fast and can translate thousands of words per minute.
  • It can translate into multiple languages at once which drastically reduces the amount of manpower needed.
  • Implementing MT into your localization process can do the heavy lifting for translators and free up their valuable time, allowing them to focus on the more intricate aspects of translation.
  • MT technology is developing rapidly, and is constantly advancing towards producing higher quality translations and reducing the need for post-editing.

There are many advantages of using MT but we can’t ignore the disadvantages. MT does not always produce perfect translations. Unlike human translators, computers can’t understand context and culture, therefore MT can’t be used to translate anything and everything. Sometimes MT alone is suitable, while others a combination of MT and human translation is best. Sometimes it is not suitable at all. MT is not a one-size-fits-all translation solution.

When Should You Use Machine Translation?

When translating creative or literary content, MT is not a suitable choice. This can also be the case when translating culturally specific-texts. A good rule of thumb is the more complex your content is, the less suitable it is for MT.

For large volumes of content, especially if it has a short turnaround time, MT is very effective. If accuracy is not vital, MT can produce suitable translations at a fraction of the cost. Customer reviews, news monitoring, internal documents, and product descriptions are all good candidates.

Using a combination of MT along with a human translator post-editor opens the doors to a wider variety of suitable content.

Which MT Engine Should You Use?

Not all MT engines are created equal, but there is no specific MT engine for a specific kind of content. Publicly available MT engines are designed to be able to translate most types of content, however, with custom MT engines the training data can be tailored to a specific domain or content types.

Ultimately, choosing an MT engine is a process. You need to choose the kind of content you wish to translate, review security and privacy policies, run tests on text samples, choose post-editors, and several other considerations. The key is to do your research before making a decision. And, if you are using a translation management system (TMS) be sure it is able to support your chosen MT engine.

Using Machine Translation and a Translation Management System

You can use MT on its own, but to get the maximum benefits we suggest integrating it with a TMS. With these technologies integrated, you will be able to leverage additional tools such as translation memories, term bases, and project management features to help streamline and optimize your localization strategy. You will have greater control over your translations, and be able to analyze the effectiveness of your MT engine.

Reference: http://bit.ly/2P85d7P

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The Story of WordPress is the Story of Localization

The Story of WordPress is the Story of Localization

Ciklopea published an interview with  Emanuel Blagonić to share his experiences and views on WordPress web development and website localization solutions.

Would you please introduce yourself?

My name is Emanuel Blagonić. I like to say that I am, first and foremost, a father. I am a designer, I design user interfaces and work with small and big companies from Croatia and worldwide. I first encountered WordPress more than 12 years ago when the CMS was very different from what it is now. I have been introducing the benefits of WordPress to the people ever since, which has taken a form of active participation in the Croatian and global WordPress communities over the past few years.

What is the importance of localization in your opinion?

The story of WordPress is the story of localization. The first major success occurred in Japan which was home to the strongest localization community of the world in those days (15 years ago). Localization is the key to success if we want to make something more available to worldwide audiences. The WordPress mission is to democratise publishing, and to do that we need to make the software available to everyone – regardless of their proficiency in foreign languages – and this is where localization is coming to the fore.

What are your experiences with WordPress localization (challenges, solutions, etc.)?

There are two types of localization. One type includes WordPress software localization. WordPress relies on its – at times almost fanatical – worldwide community in that respect. WordPress is actively translated at home, at work, and at the events known as Contributor Days, where many people working in different teams meet to contribute to the WordPress project. More than 500 people attended the Contributor Day recently held in Belgrade as part of the WordCamp Europe conference (currently the largest WordPress conference in the world), who actively contributed to the community throughout the day. One type of contribution is the localization of main software, plugins, themes and more.

Content localization is the second part of the localization story. Content localization is important in terms of target audiences of a specific website. It is important to localize your website for your target audiences to make your content, message or your products and services available to as many people as possible.

What are your experiences with the localization on other CMS platforms?

I don’t really have experience with other CMS platforms so I can’t claim whether localization is implemented better or worse. The basic problem of WordPress – if that can be seen as a problem at all – is that, unlike some other CMS platforms, localization is not an integral part of its installation, but it needs to be “upgraded” through plugins (such as WPML). Although that’s not really an issue, I believe localization could be solved better under WordPress. However, the facts that 30 % of all the world’s websites are powered by WordPress (its share among the CMS platforms has long been larger than 50 %) and that it is the most popular CMS of them all makes the case for WordPress content localization being an important part of your online strategy.

Is localization process affected by website complexity?

It surely is. You can opt for different approaches to localization based on the website “size”, i.e. its complexity. The users most frequently choose WPML as the most professional WordPress localization plugin, and those who do not want to pay for it (WPML is a premium plugin) opt for other solutions.

As always, every solution has its pros and cons, and with very complex websites it is perhaps best to consider a customized solution with each language being a separate WordPress (multisite) installation where content connection will be solved with custom software code.

WPML is the genuine commercial solution that provides a wide range of possibilities to translation agencies who use specific tools, making the localization process faster and smoother.

Is localization something to be considered before or after the development?

Definitely before. Although it is possible to localize a website after it has been published, I believe that having an initial plan of what needs to be done today, tomorrow and the day after tomorrow always pays off. If we have a vision for the next two or three years, in addition to being able to make an easier prediction on the type of content that we need, we will also have a better insight into the localization requirements. Based on that, you will be able to choose a better solution, of course, in collaboration with your web developers.

Why is it important to choose a professional LSP for website localization?

As always, having professional and reliable partners is important. Although we tend to believe that if we can understand and speak, say, English, we can also translate content, we should be aware that a self-service translation (mostly) does not meet the expectations of our audiences on the target markets. If you are targeting British, German, Italian, French, Russian or any other market for that matter, it is important to have a professional translation because your website in most cases serves as your reflection and the place of first contact with your potential clients. Of course, this is something that leaves an impression, so it is important to make the impression you want.

Reference: http://bit.ly/2LAQkvU

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Mastering the art of Transcreation

Mastering the art of Transcreation

Former British beauty queen, glamour model and celebrity Danielle Lloyd wanted a classy tattoo. Aside from the fact that classy tattoo is an oxymoron of the first order, her head-on collision with Latin is an object lesson in the importance of good translation. Her shoulder was supposed to read “To diminish me will only make me stronger.” It actually translates as “As who am I wearing away for myself, I only set (it) down for/on myself, strong man (that I am).”

Lloyd is far from alone in having nonsense inked into her skin. Another example that did the rounds on social media is the unfortunate woman who wanted to write, “I love [name of boyfriend],” down her spine in Hebrew, but ended up with, “Babylon is the world’s leading dictionary and translation software,” instead.

As famed oilwell firefighter Red Adair is credited with saying, if you think it’s expensive to hire a professional to do the job, wait till you hire an amateur. Yet, allegedly, many leading brands do go for the cheap – or perhaps, more accurately, the unthinking – option when it comes to translation.

Good translation doesn’t just mean faithful or accurate transcription from one language to another. If it did, Coca Cola in China would be known as, “Mare stuffed with wax,” or “Bite the wax tadpole,” which is what the Chinese characters that together make the sound ‘Coca Cola’ mean depending on the dialect (Chinese characters have both a sound and a meaning). Instead it has a different name pronounced “Kokou-Kolay”, which means, “A pleasure in the mouth.” Experts in the field refer to this highly successful strategy as transcreation rather than mere translation, or intelligent localisation.

UK-based localisation agency Conversis CEO and managing director Gary Muddyman says, “Translation is just one element of global brand management. A lot of time is invested coming up with brands and communications pieces for all global brands, in terms of visuals and graphics, but also the words. All that hard work is lost if you then do a poor translation job.

People tend to think of translation as very simple conversion; you take a load of words and you put the appropriate words for that language into a sentence and it works out. It isn’t and it doesn’t. It’s much more complicated than that, particularly when you’re talking about external communications and specifically marketing and brand pieces.

“It’s as much about look and feel as cultural adaptation. Of course, there will also be legal and regulatory differences from country to country. There will be market norms that are different from country to country, distribution differences and so on. All of that would come into localisation. Translation is purely and simply the conversion of the words. Yes, it is important to brands, but only as part of a basket of considerations that you need to make.”

Muddyman was head of corporate development at HSBC and was involved in the world’s local bank brand initiative. “After nearly 500 man hours of work in coming up with the concept, nobody mentioned translation once,” he sighs. “That’s why I’m in the translation industry. I was the person who had to catch the ball, to try to work through that particular challenge and realise I didn’t even know where to start.”

Thankfully times have changed and some companies have developed a more thoughtful and enlightened approached to adapt their messaging to the panoply of international languages. Nick Parker, strategy partner at London-based brand language agency the Writer, believes there is growing recognition of the subliminal benefits of communicating like a native speaker. “Even the effort put in to translating your message appropriately is important – it says to your customers that you’ve gone to the trouble of getting it right, which increases the strength of your relationship, Customers like and trust brands more that make the effort even if they make mistakes along the way,” he says.

Parker adds, “Look at Google. All its terms and conditions are archived so you can see how its language and tone has changed over time. It has got friendlier as time has gone on, the information is simpler and clearer, and it sounds more Google now. That rarely happens naturally. It usually is the result of a lot of work.” And investment.

Google was cited as an example by several experts in the field and all agree that it takes time and a whole lot of money to do it right. Language and transcreation agency thebigword’s chief commercial officer Josh Gould says, “Google is a multi- billion dollar company trading across a number of markets but with the same approachable, conversational tone in all. It does more training than any other company I’ve ever met. It really invests in its people and its suppliers people so that linguists speaking or writing as Google are well trained, aligned and motivated. It works.”

All those who praised Google noted that it is a tough gig with incredibly high standards. “They tell [recruits] it is Harvard. Not the Harvard of the business world, but Harvard period. The majority who enter fail,” notes a well-placed source. “It’s rigorous all right.”
This alludes to the central and somewhat thorny issue of control. Some characterise Google as an uber-controlling organisation that delivers consistency of brand experience through strict discipline. Others believe its investment of time and money in staff empowers them – liberates them – to deliver consistency by living the brand values.

Whichever the case, the spectrum of control is a pertinent consideration. “We organised a summit for the all the brand language heads,” says Parker. “And while people from BT and PWC were talking about detailed guidelines, policies and process, the guy from Innocent was explaining how he spent four months looking for a Norwegian writer whose style and tone fitted with the brand’s ethos.”

Control versus empowerment is never going to be easy to answer, or even possible given the vagaries of business and the nature of the organisation for whom one works. Both strive for consistency in delivery, which is what every brand custodian wants, but any marketer worth his or her salt also wants the brand to be meaningful to its audience, and that requires a much greater degree of flexibility.

McDonald’s has invested billions in installing its strapline, “I’m lovin’ it,” in the global consciousness and for such a brand you’d expect consistency to be king, queen and all the courtiers and for that particular phrase to be used exactly as is around the world. However, that would be a mistake in China as love is a serious word. Traditionally the word is never said aloud. Even today lovers use, “I like you” to communicate great affection without actually saying love, according to global transcreation agency Mother Tongue’s CEO Guy Gilpin’s marvelous volume ‘The Little Book of Transcreation.’ McDonald’s accepted the need to adapt and opted instead for, “I just like (it),” which is more normal, more everyday vocabulary, easier on Chinese ears and retains the youthful, confident street vibe of the English original.

There are many examples where constancy of global messaging or positioning would have been a mistake. A campaign by Intel in Brazil demonstrates the point. The English slogan, “Sponsors of Tomorrow,” translated directly in Portuguese would imply that the brand doesn’t yet deliver on its promises. “In love with tomorrow,” stays true to the values expressed in the rational original English line, but importantly is much more in keeping with a Brazilian population falling more and more in love with the latest high tech products.

When Motorola launched its Q phone in Canada it hadn’t foreseen the hilarity with which its marketing messaging would be received by French speakers. ‘Q’ sounds much like ‘cul’ – that is ass – in French. Lines like, “L’intelligence renouvelee” and “Si c’est important pour vous, c’est important pour votre Q,” in common parlance became, “My ass. Renewed intelligence,” and, “If it’s important to you, it’s important to your ass.” Pepsi’s unwitting claim to rejuvenate the dead went down in the annals of advertising history as how not to pull off a global campaign. “Come alive with Pepsi!” actually means “Pepsi. Bring your ancestors back from the dead” in Chinese.

Haribo is an institution in its home market, Germany, and its strapline, “Haribo macht Kinder froh, und Erwachsene ebenso,” works perfectly there. Literally translated it becomes the stilted, dry and decidedly unmotivating, “Haribo makes kids happy, and adults too.” It doesn’t even rhyme, damn it. How much more appropriate for the brand is the reimagined UK version? “Kids and grown-ups love it so, the happy world of Haribo.”

Translating jingles can be a nightmare, as Gillette found. The German translation of, “The best a man can get,” comes out as, “For the best inside a man,” which doesn’t make a whole lot of sense given that facial hair is on the outside, not the inside of a man. In addition, the line was too short for the music and so had to be dragged out for longer than sounds natural. And it doesn’t even rhyme with Gillette. The tortured and tortuous result: “Fur das Be-e-e-est-e-e im Ma-a-an” became a national laughing stock.

This is where transcreation comes into its own. Transcreation is the process of adapting the messaging to resonate meaningfully with the local target audience while staying true to the meaning of the original and maintaining its intent, style, tone and context. As Gilpin puts it, “Transcreation allows brands to walk the fine line that ensures they are both fresh and relevant locally, and at the same time consistent globally.”

BMW has used the tagline, “Freude am Fahren” (Pleasure in driving) since 1969 in Germany. “Would that be as effective as ‘The Ultimate Driving Machine’? Same idea, different words – that’s what transcreation is all about,” Gilpin says. Maintaining a consistent tone of voice across your communications across different languages and regions is a challenge as Parker points out. “Your brand may have adopted a friendly, approachable tone of voice, so you have to look at what are the indicators of friendliness in the particular language you are translating into. Charming has a very specific feel in English; how do you work out what is charming in Swiss German? And what if it charming in Swiss German turns out to be too idiosyncratic for your brand positioning?” he says. “There are no easy answers – it takes experience, research and creativity.”

Transcreation is about far more than words. Colours mean different things to different people. In the western world yellow is associated with cowardice, whereas in Japan it signifies courage. White is for weddings in the west and funerals in Asia. Red symbolises purity in India and passion in Europe. Lifestyle imagery must be sensitive to the audience’s experience, which is easy to observe and difficult to execute.

And don’t think going down the visual only route is a get out of jail free card. A major pharmaceutical company decided to do the visual only treatment for the international launch of a new product using pictures to explain the benefit. On the left, the ill patient; middle picture of patient taking medicine and the final shot on the right showing him recovered. The problem with that was potential customers in the United Arab Emirates read right to left.

Anomalous though it is, it may be necessary for your brand to look and sound different, and say different things to different people, in order to maintain brand consistency. “There’s a fine line between brand dilution and true localisation,” says Gould. “But without localisation you are potentially harming your business and your brand, from credibility to sales – and you may never know by how much.”

Reference: https://bit.ly/2Krw9jm

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Machine Translation From the Cold War to Deep Learning

Machine Translation From the Cold War to Deep Learning

In the beginning

The story begins in 1933. Soviet scientist Peter Troyanskii presented “the machine for the selection and printing of words when translating from one language to another” to the Academy of Sciences of the USSR. The invention was super simple — it had cards in four different languages, a typewriter, and an old-school film camera.

The operator took the first word from the text, found a corresponding card, took a photo, and typed its morphological characteristics (noun, plural, genitive) on the typewriter. The typewriter’s keys encoded one of the features. The tape and the camera’s film were used simultaneously, making a set of frames with words and their morphology.

Despite all this, as often happened in the USSR, the invention was considered “useless”. Troyanskii died of Stenocardia after trying to finish his invention for 20 years. No one in the world knew about the machine until two Soviet scientists found his patents in 1956.

It was at the beginning of the Cold War. On January 7th 1954, at IBM headquarters in New York, the Georgetown–IBM experiment started. The IBM 701 computer automatically translated 60 Russian sentences into English for the first time in history.

However, the triumphant headlines hid one little detail. No one mentioned the translated examples were carefully selected and tested to exclude any ambiguity. For everyday use, that system was no better than a pocket phrasebook. Nevertheless, this sort of arms race launched: Canada, Germany, France, and especially Japan, all joined the race for machine translation.

The race for machine translation

The vain struggles to improve machine translation lasted for forty years. In 1966, the US ALPAC committee, in its famous report, called machine translation expensive, inaccurate, and unpromising. They instead recommended focusing on dictionary development, which eliminated US researchers from the race for almost a decade.

Even so, a basis for modern Natural Language Processing was created only by the scientists and their attempts, research, and developments. All of today’s search engines, spam filters, and personal assistants appeared thanks to a bunch of countries spying on each other.

Rule-based machine translation (RBMT)

The first ideas surrounding rule-based machine translation appeared in the 70s. The scientists peered over the interpreters’ work, trying to compel the tremendously sluggish computers to repeat those actions. These systems consisted of:

  • Bilingual dictionary (RU -> EN)
  • A set of linguistic rules for each language (For example, nouns ending in certain suffixes such as -heit, -keit, -ung are feminine)

That’s it. If needed, systems could be supplemented with hacks, such as lists of names, spelling correctors, and transliterators.

PROMPT and Systran are the most famous examples of RBMT systems. Just take a look at the Aliexpress to feel the soft breath of this golden age.

But even they had some nuances and subspecies.

Direct Machine Translation

This is the most straightforward type of machine translation. It divides the text into words, translates them, slightly corrects the morphology, and harmonizes syntax to make the whole thing sound right, more or less. When the sun goes down, trained linguists write the rules for each word.

The output returns some kind of translation. Usually, it’s quite crappy. It seems that the linguists wasted their time for nothing.

Modern systems do not use this approach at all, and modern linguists are grateful.

Transfer-based Machine Translation

In contrast to direct translation, we prepare first by determining the grammatical structure of the sentence, as we were taught at school. Then we manipulate whole constructions, not words, afterwards. This helps to get quite decent conversion of the word order in translation. In theory.

In practice, it still resulted in verbatim translation and exhausted linguists. On the one hand, it brought simplified general grammar rules. But on the other, it became more complicated because of the increased number of word constructions in comparison with single words.

Interlingual Machine Translation

In this method, the source text is transformed to the intermediate representation, and is unified for all the world’s languages (interlingua). It’s the same interlingua Descartes dreamed of: a meta-language, which follows the universal rules and transforms the translation into a simple “back and forth” task. Next, interlingua would convert to any target language, and here was the singularity!

Because of the conversion, Interlingua is often confused with transfer-based systems. The difference is the linguistic rules specific to every single language and interlingua, and not the language pairs. This means, we can add a third language to the interlingua system and translate between all three. We can’t do this in transfer-based systems.

It looks perfect, but in real life it’s not. It was extremely hard to create such universal interlingua — a lot of scientists have worked on it their whole lives. They’ve not succeeded, but thanks to them we now have morphological, syntactic, and even semantic levels of representation. But the only Meaning-text theory costs a fortune!

The idea of intermediate language will be back. Let’s wait awhile.

As you can see, all RBMT are dumb and terrifying, and that’s the reason they are rarely used unless for specific cases (like the weather report translation, and so on). Among the advantages of RBMT, often mentioned are its morphological accuracy (it doesn’t confuse the words), reproducibility of results (all translators get the same result), and the ability to tune it to the subject area (to teach economists or terms specific to programmers, for example).

Even if anyone were to succeed in creating an ideal RBMT, and linguists enhanced it with all the spelling rules, there would always be some exceptions: all the irregular verbs in English, separable prefixes in German, suffixes in Russian, and situations when people just say it differently. Any attempt to take into account all the nuances would waste millions of man hours.

And don’t forget about homonyms. The same word can have a different meaning in a different context, which leads to a variety of translations. How many meanings can you catch here: I saw a man on a hill with a telescope?

Languages did not develop based on a fixed set of rules — a fact which linguists love. They were much more influenced by the history of invasions in past three hundred years. How could you explain that to a machine?

Forty years of the Cold War didn’t help in finding any distinct solution. RBMT was dead.

Example-based Machine Translation (EBMT)

Japan was especially interested in fighting for machine translation. There was no Cold War, but there were reasons: very few people in the country knew English. It promised to be quite an issue at the upcoming globalization party. So the Japanese were extremely motivated to find a working method of machine translation.

Rule-based English-Japanese translation is extremely complicated. The language structure is completely different, and almost all words have to be rearranged and new ones added. In 1984, Makoto Nagao from Kyoto University came up with the idea of using ready-made phrases instead of repeated translation.

Let’s imagine that we have to translate a simple sentence — “I’m going to the cinema.” And let’s say we’ve already translated another similar sentence — “I’m going to the theater” — and we can find the word “cinema” in the dictionary.

All we need is to figure out the difference between the two sentences, translate the missing word, and then not screw it up. The more examples we have, the better the translation.

I build phrases in unfamiliar languages exactly the same way!

EBMT showed the light of day to scientists from all over the world: it turns out, you can just feed the machine with existing translations and not spend years forming rules and exceptions. Not a revolution yet, but clearly the first step towards it. The revolutionary invention of statistical translation would happen in just five years.

Statistical Machine Translation (SMT)

In early 1990, at the IBM Research Center, a machine translation system was first shown which knew nothing about rules and linguistics as a whole. It analyzed similar texts in two languages and tried to understand the patterns.

The idea was simple yet beautiful. An identical sentence in two languages split into words, which were matched afterwards. This operation repeated about 500 million times to count, for example, how many times the word “Das Haus” translated as “house” vs “building” vs “construction”, and so on.

If most of the time the source word was translated as “house”, the machine used this. Note that we did not set any rules nor use any dictionaries — all conclusions were done by machine, guided by stats and the logic that “if people translate that way, so will I.” And so statistical translation was born.

The method was much more efficient and accurate than all the previous ones. And no linguists were needed. The more texts we used, the better translation we got.

There was still one question left: how would the machine correlate the word “Das Haus,” and the word “building” — and how would we know these were the right translations?

The answer was that we wouldn’t know. At the start, the machine assumed that the word “Das Haus” equally correlated with any word from the translated sentence. Next, when “Das Haus” appeared in other sentences, the number of correlations with the “house” would increase. That’s the “word alignment algorithm,” a typical task for university-level machine learning.

The machine needed millions and millions of sentences in two languages to collect the relevant statistics for each word. How did we get them? Well, we decided to take the abstracts of the European Parliament and the United Nations Security Council meetings — they were available in the languages of all member countries and were now available for download at UN Corporaand Europarl Corpora.

Word-based SMT

In the beginning, the first statistical translation systems worked by splitting the sentence into words, since this approach was straightforward and logical. IBM’s first statistical translation model was called Model one. Quite elegant, right? Guess what they called the second one?

Model 1: “the bag of words”

Model one used a classical approach — to split into words and count stats. The word order wasn’t taken into account. The only trick was translating one word into multiple words. For example, “Der Staubsauger” could turn into “Vacuum Cleaner,” but that didn’t mean it would turn out vice versa.

Here’re some simple implementations in Python: shawa/IBM-Model-1.

Model 2: considering the word order in sentences

The lack of knowledge about languages’ word order became a problem for Model 1, and it’s very important in some cases.

Model 2 dealt with that: it memorized the usual place the word takes at the output sentence and shuffled the words for the more natural sound at the intermediate step. Things got better, but they were still kind of crappy.

Model 3: extra fertility

New words appeared in the translation quite often, such as articles in German or using “do” when negating in English. “Ich will keine Persimonen” → “I donot want Persimmons.” To deal with it, two more steps were added to Model 3.

  • The NULL token insertion, if the machine considers the necessity of a new word
  • Choosing the right grammatical particle or word for each token-word alignment

Model 4: word alignment

Model 2 considered the word alignment, but knew nothing about the reordering. For example, adjectives would often switch places with the noun, and no matter how good the order was memorized, it wouldn’t make the output better. Therefore, Model 4 took into account the so-called “relative order” — the model learned if two words always switched places.

Model 5: bugfixes

Nothing new here. Model 5 got some more parameters for the learning and fixed the issue with conflicting word positions.

Despite their revolutionary nature, word-based systems still failed to deal with cases, gender, and homonymy. Every single word was translated in a single-true way, according to the machine. Such systems are not used anymore, as they’ve been replaced by the more advanced phrase-based methods.

Phrase-based SMT

This method is based on all the word-based translation principles: statistics, reordering, and lexical hacks. Although, for the learning, it split the text not only into words but also phrases. These were the n-grams, to be precise, which were a contiguous sequence of n words in a row.

Thus, the machine learned to translate steady combinations of words, which noticeably improved accuracy.

The trick was, the phrases were not always simple syntax constructions, and the quality of the translation dropped significantly if anyone who was aware of linguistics and the sentences’ structure interfered. Frederick Jelinek, the pioneer of the computer linguistics, joked about it once: “Every time I fire a linguist, the performance of the speech recognizer goes up.”

Besides improving accuracy, the phrase-based translation provided more options in choosing the bilingual texts for learning. For the word-based translation, the exact match of the sources was critical, which excluded any literary or free translation. The phrase-based translation had no problem learning from them. To improve the translation, researchers even started to parse the news websites in different languages for that purpose.

Starting in 2006, everyone began to use this approach. Google Translate, Yandex, Bing, and other high-profile online translators worked as phrase-based right up until 2016. Each of you can probably recall the moments when Google either translated the sentence flawlessly or resulted in complete nonsense, right? The nonsense came from phrase-based features.

The good old rule-based approach consistently provided a predictable though terrible result. The statistical methods were surprising and puzzling. Google Translate turns “three hundred” into “300” without any hesitation. That’s called a statistical anomaly.

Phrase-based translation has become so popular, that when you hear “statistical machine translation” that is what is actually meant. Up until 2016, all studies lauded phrase-based translation as the state-of-the-art. Back then, no one even thought that Google was already stoking its fires, getting ready to change our whole image of machine translation.

Syntax-based SMT

This method should also be mentioned, briefly. Many years before the emergence of neural networks, syntax-based translation was considered “the future or translation,” but the idea did not take off.

The proponents of syntax-based translation believed it was possible to merge it with the rule-based method. It’s necessary to do quite a precise syntax analysis of the sentence — to determine the subject, the predicate, and other parts of the sentence, and then to build a sentence tree. Using it, the machine learns to convert syntactic units between languages and translates the rest by words or phrases. That would have solved the word alignment issue once and for all.

The problem is, the syntactic parsing works terribly, despite the fact that we consider it solved a while ago (as we have the ready-made libraries for many languages). I tried to use syntactic trees for tasks a bit more complicated than to parse the subject and the predicate. And every single time I gave up and used another method.

Let me know in the comments if you succeed using it at least once.

Neural Machine Translation (NMT)

A quite amusing paper on using neural networks in machine translation was published in 2014. The Internet didn’t notice it at all, except Google — they took out their shovels and started to dig. Two years later, in November 2016, Google made a game-changing announcement.

The idea was close to transferring the style between photos. Remember apps like Prisma, which enhanced pictures in some famous artist’s style? There was no magic. The neural network was taught to recognize the artist’s paintings. Next, the last layers containing the network’s decision were removed. The resulting stylized picture was just the intermediate image that network got. That’s the network’s fantasy, and we consider it beautiful.

If we can transfer the style to the photo, what if we try to impose another language to a source text? The text would be that precise “artist’s style,” and we would try to transfer it while keeping the essence of the image (in other words, the essence of the text).

Imagine I’m trying to describe my dog — average size, sharp nose, short tail, always barks. If I gave you this set of the dog’s features, and if the description was precise, you could draw it, even though you have never seen it.

Now, imagine the source text is the set of specific features. Basically, it means that you encode it, and let the other neural network decode it back to the text, but, in another language. The decoder only knows its language. It has no idea about of the features’ origin, but it can express them in, for example, Spanish. Continuing the analogy, it doesn’t matter how you draw the dog — with crayons, watercolor or your finger. You paint it as you can.

Once again — one neural network can only encode the sentence to the specific set of features, and another one can only decode them back to the text. Both have no idea about the each other, and each of them knows only its own language. Recall something? Interlingua is back. Ta-da.

The question is, how do we find those features? It’s obvious when we’re talking about the dog, but how to deal with the text? Thirty years ago scientists already tried to create the universal language code, and it ended in a total failure.

Nevertheless, we have deep learning now. And that’s its essential task! The primary distinction between the deep learning and classic neural networks lays precisely in the ability to search for those specific features, without any idea of their nature. If the neural network is big enough, and there are a couple of thousand video cards at hand, it’s possible to find those features in the text as well.

Theoretically, we can pass the features gotten from the neural networks to the linguists, so that they can open brave new horizons for themselves.

The question is, what type of neural network should be used for encoding and decoding? Convolutional Neural Networks (CNN) fit perfectly for pictures since they operate with independent blocks of pixels.

But there are no independent blocks in the text — every word depends on its surroundings. Text, speech, and music are always consistent. So recurrent neural networks (RNN) would be the best choice to handle them, since they remember the previous result — the prior word, in our case.

Now RNNs are used everywhere — Siri’s speech recognition (it’s parsing the sequence of sounds, where the next depends on the previous), keyboard’s tips (memorize the prior, guess the next), music generation, and even chatbots.

In two years, neural networks surpassed everything that had appeared in the past 20 years of translation. Neural translation contains 50% fewer word order mistakes, 17% fewer lexical mistakes, and 19% fewer grammar mistakes. The neural networks even learned to harmonize gender and case in different languages. And no one taught them to do so.

The most noticeable improvements occurred in fields where direct translation was never used. Statistical machine translation methods always worked using English as the key source. Thus, if you translated from Russian to German, the machine first translated the text to English and then from English to German, which leads to a double loss.

Neural translation doesn’t need that — only a decoder is required so it can work. That was the first time that direct translation between languages with no сommon dictionary became possible.

The conclusion and the future

Everyone’s still excited about the idea of “Babel fish” — instant speech translation. Google has made steps towards it with its Pixel Buds, but in fact, it’s still not what we were dreaming of. The instant speech translation is different from the usual translation. You need to know when to start translating and when to shut up and listen. I haven’t seen suitable approaches to solve this yet. Unless, maybe, Skype…

And here’s one more empty area: all the learning is limited to the set of parallel text blocks. The deepest neural networks still learn at parallel texts. We can’t teach the neural network without providing it with a source. People, instead, can complement their lexicon with reading books or articles, even if not translating them to their native language.

If people can do it, the neural network can do it too, in theory. I found only one prototype attempting to incite the network, which knows one language, to read the texts in another language in order to gain experience. I’d try it myself, but I’m silly. Ok, that’s it.

Reference: https://bit.ly/2HCmT6v

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The GDPR for translators: all you need to know (and do!)

The GDPR for translators: all you need to know (and do!)

1. What is the General Data Protection Regulation?

The General Data Protection Regulation, in short GDPR, is a European regulatory framework that is designed to harmonize data privacy laws across Europe. Preparation of the GDPR took four years and the regulation was finally approved by the EU Parliament on 14 April 2016. Afterwards there was a striking silence all over Europe, but with the enforcement date set on 25 May 2018 companies have worked increasingly hard in the past months to make sure that they uphold the requirements of the regulation.

The GDPR replaces the Data Protection Directive 95/46/EC. It was designed to protect and empower the data privacy of all European citizens and to reshape the way organizations approach data privacy. While the term GDPR is used all over the world, many companies have their own designation. For instance, in the Netherlands the term is translated as ‘Algemene Verordening Gegevensbescherming’ (AVG).
More information about the GDPR can be found on the special portal created by the European Union.

2. To whom does the GDPR apply?

The GDPR applies to the processing of personal data by controllers and processors in the EU. It does not matter whether the processing takes place in the EU or not. It is, however, even more extensive as it also applies to the processing of personal data of data subjects in the EU by a controller or processor who is not established in the EU when they offer goods or services to EU citizens (irrespective of whether payment is required). Finally, the GDPR applies to the monitoring of behaviour that takes place within the EU as well. If a business outside the EU processes the data of EU citizens, it is required to appoint a representative in the EU.
So in short, the GDPR applies to every instance that

  • processes personal data from EU citizens (whether they process these data in the EU or not),
  • monitors behaviour that takes place in the EU.

In fact, this means that companies inside and outside the EU that offer or sell goods or services to EU citizens (paid or not) should apply the principles.

3. Controllers, processors, data subjects?

Yes, it is confusing, but let’s keep it short:

  • Controllers are parties that control the data.
  • Processors are parties that process the data, such as third parties that process the data for … ehm controllers.
  • Data subjects are parties whose data are controlled and processed by … you guessed it.

A controller is the entity that determines the purposes, conditions and means of processing personal data. The processor processes the personal data on behalf of the controller.

4. Sounds like a business horror. Can I opt out?

Not in any easy way. Oh wait, you can by moving outside the EU, getting rid of your European clients and clients with translation jobs about their European clients, and only focus on everything that is not EU related. But staying safe is much easier for the future, although it offers considerable hassle for the time being.

5. What happens if I do not take it seriously?

Of course the European Union thought about that before you did and they included a generous clause: if you breach the GDPR, you can be fined up to 4% of your annual global turnover or €20 Million (whichever is greater). This is the maximum fine that can be imposed for the most serious infringements, like insufficient customer consent to process data or violating the core of Privacy by Design concepts.
There is a tiered approach to fines. For instance a company can be fined 2% if it does not have its records in order (article 28), if it does not notify the supervising authority and data subject (remember?) about a breach or if it does not conduct an impact assessment.

6. So adhering to the GDPR is a no-brainer?

Yes indeed. Although you certainly should use your brains. Until now it was easy to impress all parties involved by using long and unreadable contracts, but the GDPR finally puts an end to that. Companies will no longer be able to use long unintelligible terms and conditions full of legalese. They need to ask consent for processing data and the request for consent must be given in an understandable and accessible form. Consent must be clear and distinguishable from other matters and provided in an intelligible and easily accessible form, using clear and plain language. Apart from that, all data subjects (just to check) should be able to withdraw their consent as easily as they gave it.

7. So I need to involve all people for whom I process data?

Yes. You need to ask their consent, but you need to give them access to the data you hold about them as well. EU citizens from whom you collect or process data, have a few rights:

  • Right to access
    People can ask your confirmation as to whether or not personal data concerning them is being processed. They can also ask where these data are processed and for what purpose. If someone makes use of their right to access, you need to provide a copy of the personal data in an electronic format. And yes, that should happen free of charge.
  • Right to be Forgotten
    The right to be forgotten entitles the people you collect data from to require you to erase their personal data, cease further dissemination of the data, and potentially have third parties halt processing of the data. There are a few conditions however: article 17 states that the data should no longer be relevant to the original purposes for processing, or a data subject should have withdrawn his or her consent.
  • Data Portability
    The GDPR introduces the concept of data portability. This grants persons a right to receive the personal data they have previously provided about themselves in a ‘commonly us[able] and machine readable format‘. EU citizens can than transmit that data to another controller.

8. What are these personal data you are talking about?

The GDPR pivots around the concept of ‘personal data’. This is any information related to a natural person that can be used to directly or indirectly identify the person. You might think about a person’s name, photo, email address, bank details, posts on social networking websites, medical information, or a computer IP address.

9. How does this affect my translation business?

As a freelance translator or translation agency you are basically a processor. (And if you are an EU citizen you are a data subject as well, but let’s keep that out of the scope of this discussion.)
The actual impact of the GDPR on your translation business differs greatly. If you are a technical translator or literary translator, chances are that you do not process the personal data of the so-called ‘data subjects’. In that case compliance should not be a heavy burden, although you should, of course, make sure that everything is in order.
However, if you are a medical translator for instance, translating personal health records, or if you are a sworn translator, translating certificates and other personal stuff, you have somewhat more work to do.

10. Great, you made it perfectly clear. How to proceed?

The best approach to ensure compliance with the GDPR is to follow a checklist. You might chose this 5-step guide for instance. However, if that sounds too easy you might use this 10-page document with complex language to show off your GDPR skills. You will find a short summary below:

1. Get insight into your data
Understand which kind of personal data you own and look at where the data comes from, how you collected it and how you plan to use it.

2. Ask explicit consent to collect data
People need to give free, specific, informed and unambiguous consent. If someone does not respond, does not opt in themselves or is inactive, you should not consider them as having given consent. This also means you should re-consider the ways you ask for consent: chances are that your current methods to get the necessary consent are not GDPR compliant.

3. Communicate how and why you collect data
Tell your clients how you collect data, why you do that and how long you plan to retain the data. Do not forget to include which personal data you collect, how you do that, for which purpose you process them, which rights the person in question has, in what way they can complain and what process you use to send their data to third parties.
NOTE: This needs thorough consideration if you make use of the cloud (i.e. Dropbox or Google Drive) to share translations with clients or if you use cloud-based CAT tools for translation.

4. Show that you are GDPR compliant
The GDPR requires you to show that you are compliant. So identify the legal basis for data processing, document your procedures and update your privacy policy.
NOTE: If you are outsourcing translation jobs to other translators, you should sign a data processing agreement (DPA) with them.

5. Make sure you have a system to remove personal data
Imagine what happens when someone makes use of their right to access or to be forgotten. If you do not have their data readily available, you will waste your time finding it and risking still not being compliant. So make sure you have an efficient system to fulfil the rights of all those people whose data you are processing.

So, the GDPR is no joke

It is definitely not funny for any of us, but we need to comply. To be compliant or not to be compliant: that is the question. The easiest way is to do that is the required Privacy Impact Assessment, so you know which data you collect or process and what the weak links and bottlenecks are. Following an easy guide will then help to establish the necessary controls. Opting out is not an option, but making sure your data subjects (still know what they are?) are opting into is.

Reference: https://bit.ly/2L3GVZL

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A Gentle Introduction to Neural Machine Translation

A Gentle Introduction to Neural Machine Translation

One of the earliest goals for computers was the automatic translation of text from one language to another.

Automatic or machine translation is perhaps one of the most challenging artificial intelligence tasks given the fluidity of human language. Classically, rule-based systems were used for this task, which were replaced in the 1990s with statistical methods. More recently, deep neural network models achieve state-of-the-art results in a field that is aptly named neural machine translation.

In this post, you will discover the challenge of machine translation and the effectiveness of neural machine translation models.

After reading this post, you will know:

  • Machine translation is challenging given the inherent ambiguity and flexibility of human language.
  • Statistical machine translation replaces classical rule-based systems with models that learn to translate from examples.
  • Neural machine translation models fit a single model rather than a pipeline of fine-tuned models and currently achieve state-of-the-art results.

Let’s get started.

What is Machine Translation?

Machine translation is the task of automatically converting source text in one language to text in another language.

In a machine translation task, the input already consists of a sequence of symbols in some language, and the computer program must convert this into a sequence of symbols in another language.

— Page 98, Deep Learning, 2016.

Given a sequence of text in a source language, there is no one single best translation of that text to another language. This is because of the natural ambiguity and flexibility of human language. This makes the challenge of automatic machine translation difficult, perhaps one of the most difficult in artificial intelligence:

The fact is that accurate translation requires background knowledge in order to resolve ambiguity and establish the content of the sentence.

— Page 21, Artificial Intelligence, A Modern Approach, 3rd Edition, 2009.

Classical machine translation methods often involve rules for converting text in the source language to the target language. The rules are often developed by linguists and may operate at the lexical, syntactic, or semantic level. This focus on rules gives the name to this area of study: Rule-based Machine Translation, or RBMT.

RBMT is characterized with the explicit use and manual creation of linguistically informed rules and representations.

— Page 133, Handbook of Natural Language Processing and Machine Translation, 2011.

The key limitations of the classical machine translation approaches are both the expertise required to develop the rules, and the vast number of rules and exceptions required.

What is Statistical Machine Translation?

Statistical machine translation, or SMT for short, is the use of statistical models that learn to translate text from a source language to a target language gives a large corpus of examples.

This task of using a statistical model can be stated formally as follows:

Given a sentence T in the target language, we seek the sentence S from which the translator produced T. We know that our chance of error is minimized by choosing that sentence S that is most probable given T. Thus, we wish to choose S so as to maximize Pr(S|T).

— A Statistical Approach to Machine Translation, 1990.

This formal specification makes the maximizing of the probability of the output sequence given the input sequence of text explicit. It also makes the notion of there being a suite of candidate translations explicit and the need for a search process or decoder to select the one most likely translation from the model’s output probability distribution.

Given a text in the source language, what is the most probable translation in the target language? […] how should one construct a statistical model that assigns high probabilities to “good” translations and low probabilities to “bad” translations?

— Page xiii, Syntax-based Statistical Machine Translation, 2017.

The approach is data-driven, requiring only a corpus of examples with both source and target language text. This means linguists are not longer required to specify the rules of translation.

This approach does not need a complex ontology of interlingua concepts, nor does it need handcrafted grammars of the source and target languages, nor a hand-labeled treebank. All it needs is data—sample translations from which a translation model can be learned.

— Page 909, Artificial Intelligence, A Modern Approach, 3rd Edition, 2009.

Quickly, the statistical approach to machine translation outperformed the classical rule-based methods to become the de-facto standard set of techniques.

Since the inception of the field at the end of the 1980s, the most popular models for statistical machine translation […] have been sequence-based. In these models, the basic units of translation are words or sequences of words […] These kinds of models are simple and effective, and they work well for man language pairs

— Syntax-based Statistical Machine Translation, 2017.

The most widely used techniques were phrase-based and focus on translating sub-sequences of the source text piecewise.

Statistical Machine Translation (SMT) has been the dominant translation paradigm for decades. Practical implementations of SMT are generally phrase-based systems (PBMT) which translate sequences of words or phrases where the lengths may differ

— Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation, 2016.

Although effective, statistical machine translation methods suffered from a narrow focus on the phrases being translated, losing the broader nature of the target text. The hard focus on data-driven approaches also meant that methods may have ignored important syntax distinctions known by linguists. Finally, the statistical approaches required careful tuning of each module in the translation pipeline.

What is Neural Machine Translation?

Neural machine translation, or NMT for short, is the use of neural network models to learn a statistical model for machine translation.

The key benefit to the approach is that a single system can be trained directly on source and target text, no longer requiring the pipeline of specialized systems used in statistical machine learning.

Unlike the traditional phrase-based translation system which consists of many small sub-components that are tuned separately, neural machine translation attempts to build and train a single, large neural network that reads a sentence and outputs a correct translation.

— Neural Machine Translation by Jointly Learning to Align and Translate, 2014.

As such, neural machine translation systems are said to be end-to-end systems as only one model is required for the translation.

The strength of NMT lies in its ability to learn directly, in an end-to-end fashion, the mapping from input text to associated output text.

— Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation, 2016.

Encoder-Decoder Model

Multilayer Perceptron neural network models can be used for machine translation, although the models are limited by a fixed-length input sequence where the output must be the same length.

These early models have been greatly improved upon recently through the use of recurrent neural networks organized into an encoder-decoder architecture that allow for variable length input and output sequences.

An encoder neural network reads and encodes a source sentence into a fixed-length vector. A decoder then outputs a translation from the encoded vector. The whole encoder–decoder system, which consists of the encoder and the decoder for a language pair, is jointly trained to maximize the probability of a correct translation given a source sentence.

— Neural Machine Translation by Jointly Learning to Align and Translate, 2014.

Key to the encoder-decoder architecture is the ability of the model to encode the source text into an internal fixed-length representation called the context vector. Interestingly, once encoded, different decoding systems could be used, in principle, to translate the context into different languages.

… one model first reads the input sequence and emits a data structure that summarizes the input sequence. We call this summary the “context” C. […] A second mode, usually an RNN, then reads the context C and generates a sentence in the target language.

— Page 461, Deep Learning, 2016.

Encoder-Decoders with Attention

Although effective, the Encoder-Decoder architecture has problems with long sequences of text to be translated.

The problem stems from the fixed-length internal representation that must be used to decode each word in the output sequence.

The solution is the use of an attention mechanism that allows the model to learn where to place attention on the input sequence as each word of the output sequence is decoded.

Using a fixed-sized representation to capture all the semantic details of a very long sentence […] is very difficult. […] A more efficient approach, however, is to read the whole sentence or paragraph […], then to produce the translated words one at a time, each time focusing on a different part of he input sentence to gather the semantic details required to produce the next output word.

— Page 462, Deep Learning, 2016.

The encoder-decoder recurrent neural network architecture with attention is currently the state-of-the-art on some benchmark problems for machine translation. And this architecture is used in the heart of the Google Neural Machine Translation system, or GNMT, used in their Google Translate service.

… current state-of-the-art machine translation systems are powered by models that employ attention.

— Page 209, Neural Network Methods in Natural Language Processing, 2017.

Although effective, the neural machine translation systems still suffer some issues, such as scaling to larger vocabularies of words and the slow speed of training the models. There are the current areas of focus for large production neural translation systems, such as the Google system.

Three inherent weaknesses of Neural Machine Translation […]: its slower training and inference speed, ineffectiveness in dealing with rare words, and sometimes failure to translate all words in the source sentence.

— Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation, 2016.

Reference: https://bit.ly/2Cx8zxI

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How to become a localization project manager

How to become a localization project manager

Excerpts from an article with the same title, written by Olga Melnikova in Multilingual Magazine.  Olga Melnikova is a project manager at Moravia and an adjunct professor at the Middlebury Institute of International Studies. She has ten years of experience in the language industry. She holds an MA in translation and localization management and two degrees in language studies.

I decided to talk to people who have been in the industry for a while, who have seen it evolve and know where it’s going. My main question was: what should a person do to start a localization project manager career? I interviewed several experts who shared their vision and perspectives — academics, industry professionals and recruiters. I spoke with Mimi Moore, account manager at Anzu Global, a recruiting company for the localization industry; Tucker Johnson, managing director of Nimdzi Insights; Max Troyer, translation and localization management program coordinator at MIIS, and Jon Ritzdorf, senior solution architect at Moravia and an adjunct professor at the University of Maryland and at MIIS. All of them are industry veterans and have extensive knowledge and understanding of its processes.

Why localization project management?

The first question is: Why localization project management? Why is this considered a move upwards compared to the work of linguists who are the industry lifeblood? According to Renato Beninatto and Tucker Johnson’s The General Theory of the Translation Company, “project
management is the most crucial function of the LSP. Project management has the potential to most powerfully impact an LSP’s ability to add value to the language services value chain.” “Project managers are absolutely the core function in a localization company,” said Johnson. “It is important to keep in mind that language services providers do not sell translation, they sell services. Project managers are responsible for coordinating and managing all of the resources that need to be coordinated in order to deliver to the client: they are managing time, money, people and technology.


Nine times out of ten, Johnson added, the project manager is the face of the company to the client. “Face-to-face contact and building the relationship are extremely important.” This is why The General Theory of the Translation Company regards project management to be one of the core functions of any language service provider (LSP). This in no way undermines the value of all the other industry players, especially
linguists who do the actual translation work. However, the industry cannot do without PMs because “total value is much higher than the original translations. This added value is at the heart of the language services industry.” This is why clients are happy to pay higher prices to work with massive multiple services providers instead of working directly with translators.

Who are they?

The next question is, how have current project managers become project managers? “From the beginning, when the industry started 20 years
ago, there were no specialized training programs for project managers,” Troyer recounted. “So there were two ways. One is you were a translator, but wanted to do something else — become an editor, for example, or start to manage translators. The other route was people working in a business that goes global. So there were two types of people who would become project managers — former translators or people who were assigned localization as a job task.”

According to Ritzdorf, this is still the case in many companies. “I am working with project managers from three prospective clients right now, all of whom do not have a localization degree and are all in localization positions. Did they end up there because they wanted to? Maybe not. They did not end up there because they said ‘Wow, I really want to become a head of localization.’ They just ended up there by accident, like a lot of people do.”

“There are a lot of people who work in a company and who have never heard of localization, but guess what? It is their job now to do localization, and they have to figure it out all by themselves,” Moore confirmed. “When the company decides to go international, they have to find somebody to manage that,” said Ritzdorf.

Regionalization


The first to mention regionalization was Ritzdorf, and then other interviewees confrmed it exists. Ritzdorf lives on the East Coast of the
United States, but comes to the West Coast to teach at MIIS, so he sees the differences. “There are areas where localization is a thing, which means when you walk into a company, they actually know about localization. Since there are enough people who understand what localization is, they want someone with a background in it.” Silicon Valley is a great example, said Ritzdorf. MIIS is close; there is a

localization community that includes organizations like Women in Localization; and there are networking events like IMUG. “People live and
breathe localization. However, there is a totally different culture in other regions, which is very fragmented. There are tons of little companies in other parts of the US, and the situation there is different. If I am a small LSP owner in Wisconsin or Ohio, what are my chances of finding someone with a degree or experience to fill a localization position for a project manager? Extremely low. This is why I may hire a candidate who has an undergraduate degree in French literature, for example. Or in linguistics, languages — at least something.”

The recruiters’ perspective


Nimdzi Insights conducted an interesting study about hiring criteria for localization project manager positions (Figure 1). Some 75 respondents (both LSPs and clients) were asked how important on a scale of 1 to 5 a variety of qualifications are for project management positions. Te responses show a few trends. Top priorities for clients are previous localization experience and a college degree, followed by years of experience and proficiency in more than one language. Top criteria for LSPs are reputation and a college degree, also followed
by experience and proficiency in more than one language.

Moore said that when clients want to hire a localization project manager, the skills they are looking for are familiarity with computer assisted translation (CAT) tools “and an understanding of issues that can arise during localization — like quality issues, for example. Compared to
previous years, more technical skills are required by both clients and vendors: CAT tools, WorldServer, machine translation knowledge, sometimes WordPress or basic engineering. When I started, they were nice-to-haves, but certainly not mandatory.”

Technical skill is not enough, however. “Both hard and soft skills are important. You need hard skills because the industry has become a lot more technical as far as software, tools and automation are concerned. You need soft skills to deal with external and internal stakeholders, and one of the main things is working under pressure because you are juggling so many things.

Moore also mentioned some red flags that would cause Anzu not to hire a candidate. “Sometimes an applicant does not demonstrate good English skills in phone interviews. Having good communication skills is important for a client-facing position. Also, people sometimes exaggerate their skills or experience. Another red flag is if the person has a bad track record (if they change jobs every nine months, for example).” ‘

Anzu often hires for project management contract positions in large companies. “Clients usually come to us when they need a steady stream of contractors (three or six months), then in three or six months there will be other contractors. Te positions are usually project managers or testers. If you already work fulltime, a contract position may not be that attractive. However, if you are a newcomer or have just graduated, and you want to get some experience, then it is a great opportunity. You would spend three, six or 12 months at a company, and it is a very good line on the résumé.”

Do you need a localization degree? 

There is no firm answer to the question of whether or not you need a degree. If you don’t know what you should do, it can certainly help. Troyer discussed how the localization program at MIIS has evolved to ft current real-world pressures. “The program was first started in 2004, and it started small. We were first giving CAT tools, localization project management and software localization courses. This is
the core you need to become a project manager. Ten the program evolved and we introduced the introduction and then advanced levels to many courses. There are currently four or five courses focusing on translation
technology.” Recent additions to the curriculum include advanced JavaScript classes, advanced project management and program management. Natural language processing and computational linguistics will be added down the road. “The industry is driving this move because students will need skills to go in and localize Siri into many languages,” said Troyer.

The program at MIIS is a two-year master’s. It can be reduced to one year for those who already have experience. There are other degrees
available, as well as certification programs offered by institutions such as the University of Washington and The Localization Institute.

Moore said that though a localization degree is not a must, it has a distinct advantage. A lot of students have internships that give them experience. They also know tools, which makes their résumés better fit clients’ job descriptions.

However, both Troyer and Ritzdorf said you don’t necessarily need a degree. “If you have passion for languages and technology, you can get the training on your own,” said Troyer. “Just teach yourself these skills, network on your own and try to break into the industry.”

The future of localization project management

Automation, artificial intelligence and machine learning are affecting all industries, and localization is not an exception. However, all the interviewees forecast that there will be more localization jobs in the future.

According to Johnson, there is high project management turnover on the vendor side because if a person is a good manager, they never stay in this position for more than five years. “After that, they either get a job on the client’s side to make twice as much money and have a much easier job, or their LSP has to promote them to senior positions such as group manager or program director.”

“There is a huge opportunity to stop doing things that are annoying,” said Troyer. “Automation will let professionals work on the human side
of things and let the machines run 
day-to-day tasks. Letting the machine send files back and forth will allow humans to spend more time looking at texts and thinking about what questions a translator can ask. This will give them more time for building a personal relationship with the client. We are taking these innovations into consideration for the curriculum, and I often spend time during classes asking, ‘How can you automate this?’”

Moore stated that “we have seen automation change workflows over the last ten years and reduce the project manager’s workload, with files being automatically moved through each step in the localization process. Also, automation and machine translation go hand-in-hand to make the process faster, more efficient and cost-effective.”

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