[lg policy] “The sliced raw fish shoes it wishes. Google green onion thing!” : Google’s Computing Power Refines Translation Tool

Harold Schiffman haroldfs at GMAIL.COM
Tue Mar 9 15:22:50 UTC 2010

March 8, 2010
Google’s Computing Power Refines Translation Tool

MOUNTAIN VIEW, Calif. — In a meeting at Google in 2004, the discussion
turned to an e-mail message the company had received from a fan in
South Korea. Sergey Brin, a Google founder, ran the message through an
automatic translation service that the company had licensed. The
message said Google was a favorite search engine, but the result read:
“The sliced raw fish shoes it wishes. Google green onion thing!”  Mr.
Brin said Google ought to be able to do better. Six years later, its
free Google Translate service handles 52 languages, more than any
similar system, and people use it hundreds of millions of times a week
to translate Web pages and other text.

“What you see on Google Translate is state of the art” in computer
translations that are not limited to a particular subject area, said
Alon Lavie, an associate research professor in the Language
Technologies Institute at Carnegie Mellon University. Google’s efforts
to expand beyond searching the Web have met with mixed success. Its
digital books project has been hung up in court, and the introduction
of its social network, Buzz, raised privacy fears. The pattern
suggests that it can sometimes misstep when it tries to challenge
business traditions and cultural conventions.

But Google’s quick rise to the top echelons of the translation
business is a reminder of what can happen when Google unleashes its
brute-force computing power on complex problems. The network of data
centers that it built for Web searches may now be, when lashed
together, the world’s largest computer. Google is using that machine
to push the limits on translation technology. Last month, for example,
it said it was working to combine its translation tool with image
analysis, allowing a person to, say, take a cellphone photo of a menu
in German and get an instant English translation.

“Machine translation is one of the best examples that shows Google’s
strategic vision,” said Tim O’Reilly, founder and chief executive of
the technology publisher O’Reilly Media. “It is not something that
anyone else is taking very seriously. But Google understands something
about data that nobody else understands, and it is willing to make the
investments necessary to tackle these kinds of complex problems ahead
of the market.”

Creating a translation machine has long been seen as one of the
toughest challenges in artificial intelligence. For decades, computer
scientists tried using a rules-based approach — teaching the computer
the linguistic rules of two languages and giving it the necessary
dictionaries.  But in the mid-1990s, researchers began favoring a
so-called statistical approach. They found that if they fed the
computer thousands or millions of passages and their human-generated
translations, it could learn to make accurate guesses about how to
translate new texts.

It turns out that this technique, which requires huge amounts of data
and lots of computing horsepower, is right up Google’s alley.
“Our infrastructure is very well-suited to this,” Vic Gundotra, a vice
president for engineering at Google, said. “We can take approaches
that others can’t even dream of.”  Automated translation systems are
far from perfect, and even Google’s will not put human translators out
of a job anytime soon. Experts say it is exceedingly difficult for a
computer to break a sentence into parts, then translate and reassemble

But Google’s service is good enough to convey the essence of a news
article, and it has become a quick source for translations for
millions of people. “If you need a rough-and-ready translation, it’s
the place to go,” said Philip Resnik, a machine translation expert and
associate professor of linguistics at the University of Maryland,
College Park. Like its rivals in the field, most notably Microsoft and
I.B.M., Google has fed its translation engine with transcripts of
United Nations proceedings, which are translated by humans into six
languages, and those of the European Parliament, which are translated
into 23. This raw material is used to train systems for the most
common languages.

But Google has scoured the text of the Web, as well as data from its
book scanning project and other sources, to move beyond those
languages. For more obscure languages, it has released a “tool kit”
that helps users with translations and then adds those texts to its
database. Google’s offering could put a dent in sales of corporate
translation software from companies like I.B.M. But automated
translation is never likely to be a big moneymaker, at least not by
the standards of Google’s advertising business. Still, Google’s
efforts could pay off in several ways.

Because Google’s ads are ubiquitous online, anything that makes it
easier for people to use the Web benefits the company. And the system
could lead to interesting new applications. Last week, the company
said it would use speech recognition to generate captions for
English-language YouTube videos, which could then be translated into
50 other languages. “This technology can make the language barrier go
away,” said Franz Och, a principal scientist at Google who leads the
company’s machine translation team. “It would allow anyone to
communicate with anyone else.”

Mr. Och, a German researcher who previously worked at the University
of Southern California, said he was initially reluctant to join
Google, fearing it would treat translation as a side project. Larry
Page, Google’s other founder, called to reassure him. “He basically
said that this is something that is very important for Google,” Mr.
Och recalled recently. Mr. Och signed on in 2004 and was soon able to
put Mr. Page’s promise to the test.

While many translation systems like Google’s use up to a billion words
of text to create a model of a language, Google went much bigger: a
few hundred billion English words. “The models become better and
better the more text you process,” Mr. Och said.
The effort paid off. A year later, Google won a government-run
competition that tests sophisticated translation systems.

Google has used a similar approach — immense computing power, heaps of
data and statistics — to tackle other complex problems. In 2007, for
example, it began offering 800-GOOG-411, a free directory assistance
service that interprets spoken requests. It allowed Google to collect
the voices of millions of people so it could get better at recognizing
spoken English. A year later, Google released a search-by-voice system
that was as good as those that took other companies years to build.

And late last year, Google introduced a service called Goggles that
analyzes cellphone photos, matching them to a database of more than a
billion online images, including photos of streets taken for its
Street View service. Mr. Och acknowledged that Google’s translation
system still needed improvement, but he said it was getting better
fast. “The current quality improvement curve is still pretty steep,”
he said.



 Harold F. Schiffman

Professor Emeritus of
 Dravidian Linguistics and Culture
Dept. of South Asia Studies
University of Pennsylvania
Philadelphia, PA 19104-6305

Phone:  (215) 898-7475
Fax:  (215) 573-2138

Email:  haroldfs at gmail.com


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