'World Translation': Web to TM

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Globalisation has dramatically increased translation volumes. Thanks to the internet, even SMEs employing just a few people are muscling into the global market, generating more and more text to be translated. Since machine translation does not produce convincing results, the number of translators should likewise be skyrocketing. But this is not the case. How, then, can relatively few translators handle this ever-growing volume of text?

Recycling based on fuzzy logic

The invention of the translation memory (TM) has shaken to the core traditional translation, with its steadfast reliance on huge dictionaries, an approach virtually unchanged since Babylonian times. Translation memories are based on the human predilection for repetitive behaviour. Many companies and organisations continue implementing similar processes over and over again, for years, saying pretty much the same thing every time. TMs are optimal tools for hoovering up the resulting 'corporate speak' – which, statistically speaking, always consists of the same blurb. These little miracle workers operate on the basis of fuzzy logic, which can recognise variable language patterns. As a result, the costs involved in translating a company CEO's annual Christmas speech which changes relatively little on each occasion can be cut year on year.

The first commercial translation memories were produced in Stuttgart. In the late 1980s, Jochen Hummel and Iko Knyphausen, a couple of technology geeks who had set up a small translation agency in 1984, started developing various programs to support the translation process, also referred to as Computer-Aided Translation – or CAT – tools. The system is really simple: individual translated text segments are stored together with the source text in a database to form a parallel corpus. If Trados, as this now world-famous software is known, recognises a block of text, or elements of it, the memory recycles the relevant translation from the memory. In ideal cases, translators will then only need to confirm the suggested translation, speeding up their work. CAT tools can generate significant savings in repetitive texts. If car manufacturer Citroën has already produced manuals for its C2, C3, C4 and C5 models, the translation of its C8 manual will cost a lot less.

The biggest advantage of TMs is invisible: they make a company's language consistent, harmonising its documentation, sales and support material and website. This way, everything becomes straightforward and uniform. The first globally successful 'orgy of harmonisation' came in 1997 when Microsoft sought to simplify the translation of its operating system into all the world's major languages as much as possible. Bill Gates quickly bought up part of Trados, optimised the system and made Windows the most successful software of all time. Dell and other large customers followed suit. In 2005, the company was snapped up by British competitor SDL, which took over the 'Trados' brand name for its software products.

From standalone solutions to cloud-based TMs

The vast majority of translators work from home as freelancers. But until recently, high-speed data lines were more the exception than the rule. Out of necessity, individual translators created their own databases, resulting in a myriad of standalone solutions. They saw their databases as their own private property, so it was hard to motivate them to cooperate. The big agencies responded with forced collectivisation by creating a new job, namely that of 'translation manager'. The translation manager's job was to act a bit like a tax collector, demanding the submission of bilingual files after every assignment. From that time on, translators and revisers received packages with each order, containing all the key translation resources they needed. The results then had to be returned, again as a package, which the translation manager fed into the main memory.

Smart agencies were able to build up enormous memories and consolidate their market position. However, their diligence was undermined by an inherent flaw in the system: the larger the main memory became, the worse the quality of the packages. The 'concordance memory' and the glossaries, which by this stage were gigantic, were too big to fit into the package, placing individual freelancers at a serious disadvantage, compared with server users.

In 2015, SDL, the company which had taken over the Trados software, launched the first usable GroupShare server. Since then, blocks of text have been stored on a central server, so that any number of translators can access these translation resources. This server technology means there is now no limit to the recycling of translation segments. And from here it is just a small step to 'World Translation', a cloud-based TM containing every translated segment on the internet.

The digital revolution as a cost killer

The blueprint for the first fully automatic translation network, called the TTN Translation Network, was drawn up in Geneva in 1987, before the internet as we know it existed. TTN's founder, Martin Bächtold, trialled the first inter-university networks at Stanford University in Silicon Valley. The lectures he attended detailed the comparative advantage model, making it immediately obvious that translation and communication would henceforth go hand in hand. The idea was that future translations would be produced in regions offering the best price-quality ratio, where the target language was actively spoken.

When Martin flew back to Geneva, his luggage contained one of the very first modems. Using this loudly-hissing tin box, which was still banned in Switzerland at the time, the world's first translation server was installed on a Schnyder PC with a 10-Mb hard drive. But this innovation came far too early for the market. Back then, nobody knew how a modem worked. The company had to borrow money to buy devices on the cheap in Taiwan which it then shipped free of charge to its customers and translators. One of its first customers was the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) avalanche warning service, an offshoot of the Institute for Snow and Avalanche Research (SLF) in Davos. Avalanche warnings had to be translated very fast, with the resulting texts sent back in digital format, not by fax. Back then, when an avalanche bulletin arrived, translators were informed by loud fax warnings, a system long since replaced by text messages and smartphone interfaces.

Created at CERN in Geneva in 1989, the World Wide Web revolutionised communication technology by introducing a new standard. When TTN went online, the Swiss Post Office at the time assigned it the customer number 16. Profits from the first system were ploughed into the development of a kind of ARPANET for translations in India, where a huge IT team programmed the code. The idea was to use a replicated network to establish a cloud system capable of fully automatically routing 165 languages. This attempt ended in failure, because the code was overly long and the problems encountered proved far more complex than anticipated.

The second attempt was more successful, but took much longer than expected. Step by step, ever larger parts of the processes were automated, slashing production costs by 30%. It turns out that agencies using artificial intelligence can manage large customer portfolios more efficiently than their exclusively human counterparts. Their programs calculate translators' availability, taking into account their working hours and holiday absences. And this optimised time management benefits translators, who enjoy a more constant stream of work. In a nutshell, there is less stress and greater productivity.

High levels of computational power required

Patrick Boulmier, Big Data specialiste from Infologo in Geneva is working with Keybot CEO Martin Bächtold to ready the very latest supercomputers for this 'world language machine'. The aim is to convert hundreds of websites a minute into TMs.


Keybot: Web to TM

Many global multinationals take a haphazard approach to the digitalisation of translations. While these companies have web applications with thousands of translated pages, they have no translation memories with these texts neatly stored in parallel corpora. This lackadaisical approach to selecting a translation provider has disastrous consequences: specifically, when poorly organised companies set out to overhaul their website, they have to pay the full price for every page, because the work already done cannot be recycled. As a result, a lot of knowledge is being unnecessarily lost, and reacquiring it costs money.

'Web to TM' entails combing the internet and converting it into an enormous translation memory:


This gives such companies a helping hand. Keybot, a TTN subsidiary, has developed a translation search engine of the same name which scans the internet in a similar way to Google. But it only stores multilingual pages, indexing them as parallel corpora. A complex network of servers mines the data and searches potential customers' websites for translated text segments. The extracted knowledge, i.e. the 'big data', has to be cleaned and sorted and then subjected to statistical analysis. Repetitions have to be counted and their significance calculated and recorded. Only when this laborious process is complete can the machine pass on the information, piece by piece, to a battery of GroupShare servers. After this long, drawn-out procedure, whenever translators open an order with their CAT software, all the parts the search engine has found on the customer's web application will have been translated automatically. The translator always has the latest version to be published, not an outdated version subsequently edited in-house.

To enable Keybot to match elements of language, it inputs all Wikipedia pages and translations of biblical texts and human rights in 165 languages. Each language has its own 'genetic code' that can be extracted in the form of n-grams. Keybot tries to use these statistical properties to identify and create parallel texts out of these blocks of text. The system is still at the beta stage, and so far it has only been possible to create reliable TMs if a customer has structured its website in such a way that the crawler does not get confused during the input process. The largest-ever translation memory so far generated was produced for a US firm and covers 23 languages.

Keybot intends to transform the entire multilingual part of the internet into a gigantic translation memory. 'Web to TM' is the way ahead. This transformation will be highly intensive in computational terms, so can only be performed by a correspondingly huge server farm. To acquire the necessary capital, Keybot is planning an IPO on Germany's SME exchange and is trying to raise crowdfunding to finance some of the hardware.

SLOTT Translation

The decisive innovations in machine translation were prompted by the meteorological sector. Weather forecasts face a virtually insoluble dilemma: they have to be disseminated rapidly, but must contain no translation errors. The statistical approach adopted by Google Translate doesn't help in this regard, as it is too inaccurate and can never hope to reproduce the ultra-precise nature of weather warnings.

Jörg Kachelmann, a really smart weatherman who had studied mathematics in Zurich, was the first person to resolve this dilemma. He took a simple Excel sheet and put together a cell-based system capable of managing language generation. As far back as the 1980s, the head of the SLF tried, but failed, to build a machine translation system. A German university's statistical attempt harnessing the principle of probability and Markov models also failed to deliver. So years later, when Kurt Winkler, an SLF engineer based in Davos, sent a bizarre-looking Excel sheet from the Alps to that language metropolis, Geneva, at first linguists ridiculed him as the 'fool on the hill' and his project was banished to their bottom drawer, like some second-rate detective novel. It was only when he persisted that a TTN employee familiar with translation memories was given the task of checking the integrity of Winkler's solution. One incorrect sentence, and Winkler's system would have been dead and buried.

After three days, there was still no word from this TTN analyst. Amazingly, no errors were found, and even a program specially designed to elicit them was unable to detect any. Winkler, who knew nothing about linguistics, analysed the texts of weather alerts and their translations for the previous 10 years, based on potential mutations. The fruit of his efforts was an Excel database that nobody could understand.

Or COULD they? A century ago in his lectures, Geneva-born Ferdinand de Saussure, the founding father of Structuralism, had highlighted the syntagmatic structure of language. He was the first to define the potential mutations that could occur in a linguistic structure, though he did not spot the connection with other languages. Winkler broke down the texts into segments using the same principles and set transformational rules for translating text segments from one language into another.

Dr. Kurt Winkler


Machine translations for greater security


This was how the SLF's Dr Kurt Winkler made an amazing breakthrough in machine translation, enabling avalanche warnings to be translated in a fraction of a second.


Bizarre-looking Excel sheet


Using Winkler's catalogue of standard phrases, millions of idiomatically and grammatically correct sentences can be generated in four languages. However, his system only works for avalanche warnings in Switzerland, and the phrases need to be generated using an on-screen catalogue. This is not very practical and its application is extremely limited.

TTN is experimenting with an analogue system, known as SLOTT Translation. As with weather forecasts, the translations must not contain any language errors, as this would undermine customers' faith in the system. In future, communications with customers will be standardised using a catalogue of only 20 sample sentences, so that enquiries can be correctly answered in flawless language.

It is unclear whether SLOTT will be able to gain a foothold as a commercial system. However, there is no doubt that future TMs will be hierarchically organised, significantly enhancing their potential. So the next generation of CAT systems will be able to properly translate not just the exact text stored in a TM, but also millions of variants.


Free for publication (2337 words)

Martin Bächtold, Keybot LLC, Geneva, May 2017