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Google's AI translation system is approaching human-level accuracy

Google's AI translation system is approaching human-level accuracy


But there’s still significant work to be done

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Google is one of the leading providers of artificial intelligence-assisted language translation, and the company now says a new technique for doing so is vastly improving the results. The company’s AI team calls it the Google Neural Machine Translation system, or GNMT, and it initially provided a less resource-intensive way to ingest a sentence in one language and produce that same sentence in another language. Instead of digesting each word or phrase as a standalone unit, as prior methods do, GNMT takes in the entire sentence as a whole.

"The advantage of this approach is that it requires fewer engineering design choices than previous Phrase-Based translation systems," writes Quoc V. Le and Mike Schuster, researchers on the Google Brain team. When the technique was first employed, it was able to match the accuracy of those existing translation systems. Over time, however, GNMT has proved capable of both producing superior results and working at the speed required of Google’s consumer apps and services. These improvements are detailed in a new paper published this week.


In some cases, Google says its GNMT system is even approaching human-level translation accuracy. That near-parity is restricted to transitions between related languages, like from English to Spanish and French. However, Google is eager to gather more data for "notoriously difficult" use cases, all of which will help its system learn and improve over time thanks to machine learning techniques. So starting today, Google is using its GNMT system for 100 percent of Chinese to English machine translations in the Google Translate mobile and web apps, accounting for around 18 million translations per day.

Google admits that its approach still has a ways to go. "GNMT can still make significant errors that a human translator would never make, like dropping words and mistranslating proper names or rare terms," Le and Schuster explain, "and translating sentences in isolation rather than considering the context of the paragraph or page. There is still a lot of work we can do to serve our users better." But soon, as Google’s products and services continue vacuuming up valuable corner cases and rare phrasings, our phones may be capable of breaking down language barriers as effectively as a bilingual human being.