We broke the story that Do Enjoy Life Google had just published a research paper claiming a breakthrough in neural machine translation. In a blog post after we published our article, Google announced that the new Google Neural Machine Translation (GNMT) is now powering “100% of machine translations from Chinese to English—about 18 million translations per day.” More language combinations will follow.
This represents significant progress. The first time NMT runs in a production deployment at a scale and speed makes it useful to the online public. Google achieves the speed by training the model on GPUs typically used in gaming and then, which is new, executing it on its Tensor Processing Units, computer chips custom-built for artificial intelligence (AI) applications.
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The Google team was excited by the new model’s output. The paper’s title, Bridging the Gap Between Human and Machine Translation, is anything but modest. It claimed, “Our system’s translation quality approaches or surpasses all currently published results” and that “human and Google Neural Machine Translations are nearly indistinguishable.”
Yes, those quotes were plucked out in isolation and lacked the research paper’s 20 pages of context. And granted, Google tempered expectations by pointing to the “relatively simplistic and isolated sentences sampled…for the experiment.” They also blogged that “machine translation is by no means solved” and admitted, “GNMT can still make significant errors that a human translator would never make.” Still, the release was a clever example of what happens at the intersection of research and marketing—to quote Iconic Machine Translation CEO John Tinsley—and kicked off a major news cycle with the story being picked up by all the major tech blogs and even science Magazine.
For technology-savvy language service providers and translators, it creates the premise of significant growth, driven by increased translation and post-editing volumes and smarter assistive tools—Daniel Marcu We went beyond the hype. We reached out to a dozen leading researchers and practitioners in machine translation and Google’s Mike Schuster, one of the paper’s lead authors, to understand just how big a deal this is. The following experts responded to Slater’s request for comments:
Diego Bartolome, CEO of MT, a specialist in your language technology
Kirti Vashee, an independent technology and marketing consultant formerly of Asia Online
Tony O’Dowd, CEO of KantanMT
Daniel Marcu, Founder of FairTradeTranslation.com and former SDL Chief Science Officer
John Tinsley, CEO and co-founder of Iconic Translation Machines
Joss Moorkens, a post-doctoral researcher at ADAPT Center and Lecturer at Dublin City University (DCU)
Rico Sennrich, a post-doctoral researcher in the Machine Translation group at the University of Edinburgh
Juan Alonso, Head of Lucy Software MT development
Gábor Bessenyei, Managing Director at MorphoLogic Localisation
Jean Senellart, CTO of SYSTRAN
Abdessamad Echihabi, VP of Research & Development at SDL
META-NET’s Jan Hajic, Josef van Genabith, Andrejs Vasiļjevs, Georg Rehm
Mike Schuster, Research Scientist at Google
A Breakthrough?
Describing the Google announcement as “good news for everyone,” Daniel Marcu of FairTradeTranslation.com says, “For researchers, it validates that the neural trend many have embraced in the last two years is worth pursuing. For technology-savvy language service providers and translators, it creates the promise of significant growth, driven by increased translation and post-editing volumes and smarter assistive tools. For teams interested in developing their own MT systems, it provides the blueprint for an easier to replicate model—a neural engine has less hidden, non-reported ‘black magic’ than a phrase-based statistical engine.”
Marcu adds that while “training and deployment costs are still a barrier for many organizations, those are rapidly declining as well.” The only approach that will give significant improvements over current capabilities into the future—John Tinsley of Iconic says the availability of neural MT via the Internet “has substantial positive implications for Google Translate and similar online consumer applications in the short term.”
Tinsley calls NMT “the way forward” and “the only approach that will give significant improvements over current capabilities into the future for all providers.” He qualifies, however, that from a commercial and enterprise perspective, domain- or user-adaptation of MT engines “based on existing statistical and ensemble approaches” remains state of the art Graet New. He says his team has been collaborating with the ADAPT research center to compare NMT capability against Iconic’s production engines in “an apples-to-apples comparison in collaboration, ” which will be shared in the next month. The NMT field is active!”
