I attended the last TAUS meeting in Tokyo. This organization has come a long way in promoting machine translation among translation professionals, primarily translation buyers. Corporations like Microsoft, Adobe, Dell, eBay, etc., donated large bilingual data sets which allowed companies to improve the stage of machine translation, to run hundreds of tests with Moses in order to improve accuracy and find better ways in which to make machine translation a reality we find embedded and we take for granted in so many products.
Pangeanic’s drive to create and develop innovative language solutions for its clients led us to create a new section called PangeaMT, which was the first one to use Moses in a commercial setting back in 2009 and served its clients with language automation. Nowadays, it seems that widespread adoption in the wake of solutions provided by non-industry giants like Google and Microsoft have created a language solutions industry by plugging 3rd-party APIs. However, research in machine translation has come to halt due to lack of funding from institutions, although paradoxically adoption by language service professionals is far from general.
Jaap van der Meer provided a good overview of events and developments in the translation industry and language technology landscape over the last 30 years during the last TAUS Summit in Tokyo. I will summarize his review and some of the developments in language automation and machine translation during the TAUS Executive Forum Tokyo 2016, adding a few facts and spices of my own.
Language technology in the 80’s and 90’s
Starting in the 80’s, the advent of the computer meant that PCs helped translators do spellchecks and grammar checks. That alone, even at such a basic level, marked a change in the role of the translator and translation as a profession. We moved from the (IBM) typewriter to floppy disks. Software was king and the term “localization companies” began to replace “translation companies” as computing made it possible to automate translation processes. Perhaps this is a very Western view and developments took place at different times in different countries. Over these 20 years, the language technology industry saw the development of tools and concepts we still see today as computer assisted tools.
Language technology in the 21st century
Gaston Bastiaens, an ex-Philips executive and entrepreneur went to jail because of (partly) his promises of personal, wearable translator did not materialize, thus fiddling sales books and revenues. The “Star Trek translator” does exist nowadays, in different shapes – but is 15 years too late and in Japan. Mr Bastiaens’ company was a publicly traded company in Nasdaq and he had fiddled with numbers in order to make it credible that a “universal translator” was just 1 year away in a far advanced stage of development – close to commercialization. This recalls the stories of “high expectations that were not met” just as it happened with the ALPAC report in the 1950’s.
From 2000, the globalization phase and connectivity take place. There is an unhealthy accumulation of translation memories that go to a server. We want all translators to connect and work in a synchronized mode whenever possible. There are new ideas about workflow automation because managing the process of translation can be as costly if not more expensive than the translation itself. Competition begins in the CAT tool landscape: Star TransiT, OpenTM2, WordFast. We find TM client server in projects like Euramis, memoQ and Advanced Leveraging from products like MultiCorpora or Déjà Vu.
Yes, it can take up to 42 steps to get a translation job done. That’s the reason why translation companies raised $250M in venture capital money. But translation companies also inflated expectations and, to follow the well-known Gartner Hype Cycle, innovators tend to integrate in larger translation companies.
Machine Translation was back in 2007: “Let a thousand systems bloom”
Around 2010, translation becomes a strategic matter in enterprise agendas. It is the age of web services. Technology is able to build all types of APIs, there are webs-based TMs, TM and MT as a hybrid solution and MT becomes an enabler for other business. The value proposition of the language industry is technology integration.
For LSPs, this trend offers new avenues:
- Diversification of services
- Digital marketing
GMS companies become “old technologies” and are absorbed by LSPs. It is time for companies like XTRF, Lionbridge, which acquires Clay Tablet; then SDL buys Idiom, etc., in a frenzy of technology integration and lock-up. After all, if you own the tools or the channels that make it easy for your clients to connect to your services, you are on your way to dominate the market.
By 2010, more words are translated by machines than by humans.
By 2010, more words are translated by machines than by humans. Enterprises begin to use different technologies but people at the bottom of the pyramid look for new technologies and tools. The 20th century was an “export mentality”: one translation that could fit all types of content and situations. We pick a market and we have to translate for that market; we create a project and we cascade it down the supplier chain , going from English into Japanese, English into Chinese, English into German, English into Spanish, etc. However, information is multidirectional in the 21st century. Quality must be differentiated. Sometimes one may need personalized information for a single organization, individual or company. Facebook has mastered this art and there are other types of content that are directed to people that are no longer worried about a small grammar mistake or typo. We do not choose the local because users come from a variety of situations and places and they may be familiar with some languages and choose to interface with our content in a language different from their mother tongue for different reasons. Content is also “borrowed”. Today, we must be happy if somebody “borrows” content from us! We are facing a translation streaming, translation is continuous, we are approaching a stage of collaborative translation. We need to work together but in a way in which cloud-based platforms make sense. Therefore, translation is multidirectional, not English into another languages, we also need to understand what people are saying. This has led us to enter the convergence era. Machine translation has become an API: you plug it to your system and you get translated. Machine translation is expected to happen.
With the exception of Germany “the cloud is not legal”, the move is irresistible.
So maybe we, translation professionals, are still thinking we are have a “luxury” or offer a “premium” service. But the truth is that most consider translation a right and it should be free, this is a 6 billion user market that expects translation processionals to pay for the infrastructure and technology development.
Convergence is a very broad term. It can mean many things: convergence of consumer, free and paying models. Suddenly, the language technology industry has become attractive. Google was the first innovator / invader in the translation field and has actually changed the way people view machine translation. Many startups have joined the language technology landscape, offering from translator productivity tools to translation apps, even “streetwise” translators like QR translator, extremely affordable speech-to-speech translation systems.
They are exciting times for the language translation industry and translation experts in general. But, what lies ahead of us? Does the future need translators? Chris Wendt from Microsoft Corporation attended the conference in Tokyo for the second year running. He had stated that “Transcreation and adaptation between cultures will remain necessary for one more generation, until the differences between the earth’s cultures have been reduced to minor deltas that machines can bridge. But I would not recommend my children choosing human translation as a profession.”
As “professional translators” and language specialists, we need to stop and think about the 6 billion potential users. How can we best help them?