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search paper, Google reveals how it uses its own "interlingua" to internal-
ly represent phrases, regardless of the language. The resulting "zero-shot"

deep learning lets it translate a language pair with "reasonable" accuracy,

as long as it has translated them both into another common language.

The company recently switched its Translate feature to the deep-learning
Google Neural Machine Translation (GNMT) system. That's an "end-to-

end learning framework that learns from millions of examples," the com-

pany says, and has drastically improved translation quality. The problem

is, Google Translate works with 103 languages, meaning there are 5,253

language "pairs" to be translated. If you multiply that by the millions of
examples needed for training, it's insanely CPU intensive.

After training the system with several language pairs like English-to-

Japanese and English-to-Korean, researchers wondered if they could

translate a pair that the system hadn't learned yet. In other words, can the
system do a "zero-shot" translation between Japanese and Korean?

"Impressively, the answer is yes -- it can generate reasonable Korean to

Japanese translations, even though it has never been taught to do so,"

Google says.