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Google Zero-Shot Translation Gives Star Trek Fans Hope
Google's efforts to improve machine translation of languages are legendary (like Google Translate Has 51 Offline Language Packs), but now they are going well beyond what most science fiction fans are willing to suspend their disbelief for.
Zero-shot translation involves translation between language pairs that have never been explicitly seen by the system before.
This inspired us to ask the following question: Can we translate between a language pair which the system has never seen before? An example of this would be translations between Korean and Japanese where Korean⇄Japanese examples were not shown to the system. Impressively, the answer is yes — it can generate reasonable Korean⇄Japanese translations, even though it has never been taught to do so. We call this “zero-shot” translation, shown by the yellow dotted lines in the animation. To the best of our knowledge, this is the first time this type of transfer learning has worked in Machine Translation.
The success of the zero-shot translation raises another important question: Is the system learning a common representation in which sentences with the same meaning are represented in similar ways regardless of language — i.e. an “interlingua”? Using a 3-dimensional representation of internal network data, we were able to take a peek into the system as it translates a set of sentences between all possible pairs of the Japanese, Korean, and English languages.
Fans of Star Trek will believe anything - even a Universal Translator that can translate between ordinary english and ... lizardese? In the 1967 episode Arena, Captain Kirk is whisked off to a distant planet to battle a creature never encountered before by humans - a Gorn. If you're impatient, start at about 45 seconds into the trailer for this episode below. But why would you?
Here’s how it works. Let’s say we train a multilingual system with Japanese⇄English and Korean⇄English examples, shown by the solid blue lines in the animation. Our multilingual system, with the same size as a single GNMT system, shares its parameters to translate between these four different language pairs. This sharing enables the system to transfer the “translation knowledge” from one language pair to the others. This transfer learning and the need to translate between multiple languages forces the system to better use its modeling power.
(Star Trek Arena trailer)
Note that the translator works immediately. Compare to the more realistic (hah!) translator discs from Larry Niven's 1970 masterpiece Ringworld:
The tattooed one made a short speech. That was luck. The autopilot would need data before it could begin a translation...Presently the discs were filling in words and phrases...
Via Zero-Shot Translation with Google’s Multilingual Neural Machine Translation System.
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