Say What? Neural Networks Give Google Translate a Boost

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Google Translate often serves up some laughable results, but that could become a thing of the past with Google Neural Machine Translation (GNMT), which is currently at work on Chinese-to-English translations.

"Rapid advances in machine intelligence have improved our speech recognition and image recognition capabilities, but improving machine translation remains a challenging goal," Quoc V. Le and Mike Schuster, Google Brain Team research scientists, wrote in a blog post. GNMT "utilizes state-of-the-art training techniques to achieve the largest improvements to date for machine translation quality."

Google Neural Machine Translation (GNMT)

When Google Translate launched a decade ago, it relied on Phrase-Based Machine Translation. The algorithm simply looked up individual words, replaced them with the equivalent word in another language, and pieced them together for what were often incoherent sentences.

Neural Machine Translation, however, considers the entire input sentence as a unit—like you would comprehend a whole image rather than its individual pixels—taking into account the nuances of speech and meaning.

"The advantage of this approach is that it requires fewer engineering design choices than previous Phrase-Based translation systems," Le and Schuster wrote.

Google highlighted a Chinese-to-English translation produced by GNMT (above), which is just one of the more than 10,000 language pairs supported by Google Translate. "We will be working to roll out GNMT to many more of these over the coming months," Google said.

"Machine translation is by no means solved," the company concluded. GNMT can still make "significant errors" a human translator would never make, like dropping words, or mistranslating proper names and rare terms. "However, GNMT represents a significant milestone."

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