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Google launches its AI-powered Translation Hub as a cloud service

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Google today announced a new cloud service called Translation Hub that allows businesses to translate documents on a self-service basis.

An employee can enter his document in the Hub and select all languages ​​for which he wants translations. The Hub then quickly spits out the translation, preserving even the formatting of the original document (if it’s a Google Doc or Slide, PDF, or Microsoft Word document). Google says the Hub also offers management options so that users can easily research the translations and provide feedback.

“Before machine translation became dominant, customers usually sent their documents to a translation service and sometimes waited weeks for a translation,” said June Yang, Google’s VP of cloud AI and industry solutions. “Here they can do a self-service and within 30 seconds they receive a translated document back.”

The service is ideal for multinationals and companies with a global customer base. Materials science company Avery Dennison has been using a trial version of the Hub in partnership with Google for a few months now, said Murali Nathan, who leads the company’s digital innovation and employee experience group. Before moving to Translation Hub, the translation work at Avery Dennison was very decentralized.

“Each department had spent its own budget on a translation agency,” Nathan says. “And they build and maintain what’s called the translation memory,” meaning all the intelligence and experience gathered from translating Avery documents (for example, using the right choice of words, technical terms, and tone). But all that knowledge was largely stored in human brains, some outside the company.

“If the translation provider had left a person” [and] they brought in a new person, that will affect the quality of the translations,” says Nathan, noting that during the time Avery Dennison has been using the Translation Hub (with the help of some specialized language models trained in Avery-specific jargon) , he a 90% reduction in the company’s translation costs.

Google Translate was launched in 2006 and was a purely consumer-oriented product for six years. By 2012, Google realized that companies were trying to use the product for business documents, so it created an Application Programming Interface (API) for the service. “Basically, everything they got from the API was exactly what they’d get from Google Translate, just automated and supported,” said Macduff Hughes, who has been leading the Google Translate engineering team since 2012.

In 2016, machine translation received a significant boost with the emergence of neural networks. New natural language models that could translate whole sentences began to erase previous records for accuracy. Since then, Hughes says, “the translation industry has increasingly turned to (human) post-processing of machine translations as the standard way of doing translations.”

Neural networks have also opened the door to offer translations as a cloud service. As Hughes says, it turned the Translate service into “something that actually worked for professional use cases”.

After Translate debuted in the cloud, Google started adding other features. In 2018, it launched Auto ML Translation, which allowed enterprise customers to tailor translation models to their own vocabulary and stylistic requirements. This more specialized machine learning model is trained using customer-provided data, according to Google, which charges its cloud customers a translation fee per page. Generic translation without any customization costs 15 cents per page. More advanced or custom translations can cost up to 50 cents per page.

Nathan of Avery Dennison is pleased that Google’s Translation Hub has saved his company time and money, but he also sees something deeper in it. He points out that a better translation service means you can communicate with people in their own language, making them feel more valued and involved.

“I wanted to make every employee feel like a part of this organization, because that’s really essential to our D&I [diversity and inclusion]’ says Nathan. “D&I talks about diversity and inclusion, about cultural diversity, ethnic, binary, non-binary. . . but what about language diversity? It’s a dimension. And I thought that dimension is very important, especially for me as a digital employee experience leader.”

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