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Neural Machine Translation: Catch That Wave!


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Argos Multilingual

Published on

09 Jul 2018

Slator 2018 Neural Machine Translation Report

“It is tough making predictions: especially about the future.” Yogi Berra

In March 2018 published a comprehensive white paper titled: “Slator 2018 Neural Machine Translation Report”. It is a timely report that sets out to examine the current status of the translation technology, Neural Machine Translation (NMT). The report weighs up the pros and cons of the technology, looks at the growing adaption of it by big and not so big players, and looks to answer the question as to whether NMT is here for the long-term, an if so, what paradigm shifts will it cause.

Ray Kurwell, a serial inventor, says in his fascinating and thought-provoking book: “The Singularity is Near” that: “Inventing is a lot like surfing: you have to anticipate and catch the wave at just the right moment.” Of course, Kurwell was not the first to point this out. He was predated by a writer called William Shakespeare who warned that: “There is a tide in the affairs of man when taken at the flood, leads on to fortune.” The Slator report sets out to examine whether there is a tidal flood that others should now be looking to ride to their fortune.

The 37 page report, which can be purchased at, boldly declares upfront in its Executive Summary that NMT has become the “new standard in machine translation”. It bolsters this assertion by pointing out that in the last 12 months the number of NMT providers has quadrupled from a base of 5 to 20. Though perhaps more pertinent that the numbers is the quality of the companies adopting the technology as their go to language solution. In addition, NMT is being seen as a solution by major entities in both the private and public sectors. All of this painting a rosy picture of health for NMT’s future.

The report does accept that as of March 2018 NMT was still a niche movement. But the report goes on to caution that this status might be a fleeting one with exponential growth being driven at an increasing pace by the energy of the Big Tech companies and the monolithic public services entities now involved. Add to this financial potency is the fact that the technology needed is decreasing in cost and the availability of giant, clean data corpora is growing.

Many of the Big Tech behemoths such as Amazon, Google. Microsoft, IBM and SAP, to name just a few, have committed themselves to making NMT work as a solution for them. The market for these companies is global. In order to drill down in to the locales of this global opportunity they realise they need an affordable solution for the language challenges. NMT is now their champion.

Yet, as the report points out, NMT only became a player a mere four years ago. And incredibly, in those four years NMT condescended the equivalence of 15 years of statistical research in this short period of time. Google say they replaced a system that had taken them 12 years to develop by an NMT system that took them a mere 18 months to create – 1.5 percent of the time.

The report highlights a few 2017 milestones:

  1. There has been a number of key release announcements by Big Tech players such as Amazon, and by what the report calls “boutique” providers such as KantanMT.
  2. The deployment of NMT in both public and private sectors as a solution. The European Patents Office (EPO) told Slator of their satisfaction with the ability of NMT to translate huge volumes of text.
  3. NMT has proven to be effective in translating stringently controlled material such as required by the EPO, and is also proving suitable for translating massive amounts of text in a real-time environment, as with

As is the way of the business world the pricing models have not yet been fully worked out. For Big Tech companies the provision of translated texts is a service they are willing to provide their market in order to gain market share. The report says of boutique suppliers that they tend to charge “bespoke and flexible” pricing, depending on the service required. As of yet, there is no rigid matrix of pricing available. And the report points out that with all the “various ways technology is changing how translators work, the industry is likely to switch its pay model from a per word to a per hour [charge].” Tony O’Dowd, CEO of KantanMT warns the language industry that the traditional approach to translation is: “… dead (or in its twilight zone)”.

However, NMT is not being trumpeted as the panacea for all ills. For many, there are still a lot of known unknowns to be tackled. For example, there is a lively debate ongoing in the industry as to what sort of data is necessary to create a fluent NMT engine? Some argue that this is like the proverbial “how long is a piece of string” conundrum; with factors such as language pairs, subject matter, quality of data, the algorithm involved, and so on. However, Tony O’Dowd is a lot more sanguine in his approach to the debate: it’s all about the quality, he says. He believes that it is highly cleansed and aligned data, and not huge volumes of data, that is the secret to quality NMT results.

And, of course, as with all empirical matters it is not surprising that another continuing discussion is the definition of what constitutes quality. The report examines the debate around this and how exactly quality can be and should be assessed. The different players give their thoughts on this. The debate looks at technical quality testing such as the BLEU score and questions whether the ultimate assessment of quality can only be done by a human. Underlying this debate is another within the industry as to whether quality is a gold standard, never to be tarnished status; or whether quality is what the customers deems quality to be. Does an online retailer using real-time translations to communicate need the scientific precision of a life science company selling life critical equipment?

The report also brings good cheer in predicting that there will be a whole industry of sub-markets required to service the behemoths with high quality corpora. This, the reports says, is already a multi-million dollar business, and is set to grow. For some global companies it makes more sense for them to buy in ready-made corpora, than trying to create them from scratch. It is also true that many of these global companies would see the “boutiques” as a way to go for their NMT services rather than trying to build it themselves. A good example of this is the creation of the iADAATPA Consortium, an EU initiative that is tasked with developing the next generation Machine Translation platform for European Public Administrations.

Finally, as to the future of NMT? According to Kirti Vashee of SDL: “Those who best understand the overall translation production process and deploy NMT … will likely be the new leaders [of the language service industry].”

Don’t say you haven’t been warned!

Note: This blog post has been shared courtesy of KantanMT. The original source of this blog post can be found on KantanMT’s blog

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