Disclaimer:
I’m not an expert in neural networks or machine learning. Since
originally writing this article, many people with far more expertise in
these fields than myself have indicated that, while impressive, what
Google have achieved is evolutionary, not revolutionary. In the very
least, it’s fair to say that I’m guilty of anthropomorphising in parts
of the text.
I’ve
left the article’s content unchanged, because I think it’s interesting
to compare the gut reaction I had with the subsequent comments of
experts in the field. I strongly encourage readers to browse the
comments after reading the article for some perspectives more sober and
informed than my own.
In
the closing weeks of 2016, Google published an article that quietly
sailed under most people’s radars. Which is a shame, because it may just
be the most astonishing article about machine learning that I read last
year.
Don’t
feel bad if you missed it. Not only was the article competing with the
pre-Christmas rush that most of us were navigating — it was also tucked
away on Google’s Research Blog, beneath the geektastic headline Zero-Shot Translation with Google’s Multilingual Neural Machine Translation System.
This doesn’t exactly scream must read,
does it? Especially when you’ve got projects to wind up, gifts to buy,
and family feuds to be resolved — all while the advent calendar
relentlessly counts down the days until Christmas like some kind of
chocolate-filled Yuletide doomsday clock.
Luckily, I’m here to bring you up to speed. Here’s the deal.
Up
until September of last year, Google Translate used phrase-based
translation. It basically did the same thing you and I do when we look
up key words and phrases in our Lonely Planet language guides. It’s
effective enough, and blisteringly fast compared to awkwardly thumbing
your way through a bunch of pages looking for the French equivalent of
“please bring me all of your cheese and don’t stop until I fall over.”
But it lacks nuance.
Phrase-based
translation is a blunt instrument. It does the job well enough to get
by. But mapping roughly equivalent words and phrases without an
understanding of linguistic structures can only produce crude results.
This
approach is also limited by the extent of an available vocabulary.
Phrase-based translation has no capacity to make educated guesses at
words it doesn’t recognize, and can’t learn from new input.
All
that changed in September, when Google gave their translation tool a
new engine: the Google Neural Machine Translation system (GNMT). This
new engine comes fully loaded with all the hot 2016 buzzwords, like neural network and machine learning.
The
short version is that Google Translate got smart. It developed the
ability to learn from the people who used it. It learned how to make
educated guesses about the content, tone, and meaning of phrases based
on the context of other words and phrases around them. And — here’s the
bit that should make your brain explode — it got creative.
Google Translate invented its own language to help it translate more effectively.
What’s more, nobody told it to. It didn’t develop a language (or interlingua,
as Google call it) because it was coded to. It developed a new language
because the software determined over time that this was the most
efficient way to solve the problem of translation.
Stop
and think about that for a moment. Let it sink in. A neural computing
system designed to translate content from one human language into
another developed its own internal language to make the task more
efficient. Without being told to do so. In a matter of weeks.
To
understand what’s going on, we need to understand what zero-shot
translation capability is. Here’s Google’s Mike Schuster, Nikhil Thorat,
and Melvin Johnson from the original blog post:
Let’s say we train a multilingual system with Japanese⇄English and Korean⇄English examples. 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.
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.
Here
you can see an advantage of Google’s new neural machine over the old
phrase-based approach. The GMNT is able to learn how to translate
between two languages without being explicitly taught. This wouldn’t be
possible in a phrase-based model, where translation is dependent upon an
explicit dictionary to map words and phrases between each pair of
languages being translated.
And this leads the Google engineers onto that truly astonishing discovery of creation:
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.
Within a single group, we see a sentence with the same meaning but from three different languages. This means the network must be encoding something about the semantics of the sentence rather than simply memorizing phrase-to-phrase translations. We interpret this as a sign of existence of an interlingua in the network.
So
there you have it. In the last weeks of 2016, as journos around the
world started penning their “was this the worst year in living memory”
thinkpieces, Google engineers were quietly documenting a genuinely
astonishing breakthrough in software engineering and linguistics.
I just thought maybe you’d want to know.
