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takyon [soylentnews.org] writes:

Google Translate will be upgraded using a "Neural Machine Translation" technique [techcrunch.com], starting with Chinese-English translation today:

Google has been working on a machine learning translation technique for years, and today is its official debut. The Google Neural Machine Translation system, deployed today for Chinese-English queries, is a step up in complexity from existing methods. Here's how things have evolved (in a nutshell). [...] GNMT is the latest and by far the most effective to successfully leverage machine learning in translation. It looks at the sentence as a whole, while keeping in mind, so to speak, the smaller pieces like words and phrases. It's much like the way we look at an image as a whole while being aware of individual pieces — and that's not a coincidence. Neural networks have been trained to identify images and objects in ways imitative of human perception, and there's more than a passing resemblance between finding the gestalt of an image and that of a sentence.

Interestingly, there's little in there actually specific to language: The system doesn't know the difference between the future perfect and future continuous, and it doesn't break up words based on their etymologies. It's all math and stats, no humanity. Reducing translation to a mechanical task is admirable, but in a way chilling — though admittedly, in this case, little but a mechanical translation is called for, and artifice and interpretation are superfluous.

The code runs on Google's homegrown TPUs [soylentnews.org]. The Google Research Blog [googleblog.com] says that the technique will be applied to other language pairs in the coming months.

Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation [arxiv.org]

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