Microsoft taps AI techniques to bring Translator to 100 languages

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In the present day, Microsoft introduced that Microsoft Translator, its AI-powered textual content translation service, now helps greater than 100 totally different languages and dialects. With the addition of 12 new languages together with Georgian, Macedonian, Tibetan, and Uyghur, Microsoft claims that Translator can now make textual content and knowledge in paperwork accessible to five.66 billion individuals worldwide.

Its Translator isn’t the primary to assist greater than 100 languages — Google Translate reached that milestone first in February 2016. (Amazon Translate solely helps 71.) However Microsoft says that the brand new languages are underpinned by distinctive advances in AI and shall be obtainable within the Translator apps, Workplace, and Translator for Bing, in addition to Azure Cognitive Providers Translator and Azure Cognitive Providers Speech.

“100 languages is an effective milestone for us to attain our ambition for everybody to have the ability to talk whatever the language they converse,” Microsoft Azure AI chief know-how officer Xuedong Huang stated in a press release. “We are able to leverage [commonalities between languages] and use that … to enhance complete language famil[ies].”


As of right this moment, Translator helps the next new languages, which Microsoft says are natively spoken by 84.6 million individuals collectively:

  • Bashkir
  • Dhivehi
  • Georgian
  • Kyrgyz
  • Macedonian
  • Mongolian (Cyrillic)
  • Mongolian (Conventional)
  • Tatar
  • Tibetan
  • Turkmen
  • Uyghur
  • Uzbek (Latin)

Powering Translator’s upgrades is Z-code, part of Microsoft’s bigger XYZ-code initiative to mix AI fashions for textual content, imaginative and prescient, audio, and language in an effort to create AI programs that may converse, see, hear, and perceive. The crew contains a gaggle of scientists and engineers who’re a part of Azure AI and the Undertaking Turing analysis group, specializing in constructing multilingual, large-scale language fashions that assist varied manufacturing groups.

Z-code gives the framework, structure, and fashions for text-based, multilingual AI language translation for complete households of languages. Due to the sharing of linguistic parts throughout related languages and switch studying, which applies information from one process to a different associated process, Microsoft claims it managed to dramatically enhance the standard and cut back prices for its machine translation capabilities.

With Z-code, Microsoft is utilizing switch studying to maneuver past the commonest languages and enhance translation accuracy for “low-resource” languages, which refers to languages with below 1 million sentences of coaching knowledge. (Like all fashions, Microsoft’s be taught from examples in massive datasets sourced from a mix of private and non-private archives.) Roughly 1,500 recognized languages match this standards, which is why Microsoft developed a multilingual translation coaching course of that marries language households and language fashions.

Methods like neural machine translationrewriting-based paradigms, and on-device processing have led to quantifiable leaps in machine translation accuracy. However till just lately, even the state-of-the-art algorithms lagged behind human efficiency. Efforts past Microsoft illustrate the magnitude of the issue — the Masakhane project, which goals to render 1000’s of languages on the African continent routinely translatable, has but to maneuver past the data-gathering and transcription phase. Moreover, Common Voice, Mozilla’s effort to construct an open supply assortment of transcribed speech knowledge, has vetted solely dozens of languages since its 2017 launch.

Z-code language fashions are skilled multilingually throughout many languages, and that information is transferred between languages. One other spherical of coaching transfers information between translation duties. For instance, the fashions’ translation expertise (“machine translation”) are used to assist enhance their capability to grasp pure language (“pure language understanding”).

In August, Microsoft said {that a} Z-code mannequin with 10 billion parameters may obtain state-of-the-art outcomes on machine translation and cross-lingual summarization duties. In machine studying, parameters are inside configuration variables {that a} mannequin makes use of when making predictions, and their values basically — however not at all times — outline the mannequin’s talent on an issue.

Microsoft can be working to coach a 200-billion-parameter model of the aforementioned benchmark-beating mannequin. For reference, OpenAI’s GPT-3, one of many world’s largest language fashions, has 175 billion parameters.

Market momentum

Chief rival Google can be utilizing emerging AI techniques to enhance the language-translation high quality throughout its service. To not be outdone, Fb recently revealed a mannequin that makes use of a mixture of word-for-word translations and back-translations to outperform programs for greater than 100 language pairings. And in academia, MIT CSAIL researchers have offered an unsupervised mannequin — i.e., a mannequin that learns from check knowledge that hasn’t been explicitly labeled or categorized — that may translate between texts in two languages with out direct translational knowledge between the 2.

In fact, no machine translation system is ideal. Some researchers claim that AI-translated textual content is much less “lexically” wealthy than human translations, and there’s ample proof that language fashions amplify biases current within the datasets they’re skilled on. AI researchers from MIT, Intel, and the Canadian initiative CIFAR have discovered high levels of bias from language fashions together with BERT, XLNet, OpenAI’s GPT-2, and RoBERTa. Past this, Google identified (and claims to have addressed) gender bias within the translation fashions underpinning Google Translate, notably with regard to resource-poor languages like Turkish, Finnish, Persian, and Hungarian.

Microsoft, for its half, factors to Translator’s traction as proof of the platform’s sophistication. In a weblog put up, the corporate notes that 1000’s of organizations around the globe use Translator for his or her translation wants, together with Volkswagen.

“The Volkswagen Group is utilizing the machine translation know-how to serve clients in additional than 60 languages — translating greater than 1 billion phrases every year,” Microsoft’s John Roach writes. “The lowered knowledge necessities … allow the Translator crew to construct fashions for languages with restricted assets or which are endangered as a result of dwindling populations of native audio system.”


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