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Byline: Gregory M. Lamb Staff writer of The Christian Science Monitor
(MOUNTAIN VIEW, CALIF.)English rules the Internet, which can be a
frustrating thing for the world's 1.3 billion Chinese and 322 million
Spanish-speakers. They outnumber Anglophones. Even online, two-thirds
of users speak something other than English at home.
So when someone promises a smoother and easier translation program,
people around the world tend to perk up their ears. It's a step closer
to a truly "worldwide" Web where every page would be available for
everyone to read in his or her own language.
The latest step comes later this month when the National Institute of
Standards and Technology (NIST), an arm of the United States
government, announces results of its tests of several machine-
translation systems. The agency is expected to give top honors, not to
the linguistic-savvy programs at universities and elsewhere, but to a
newcomer: Internet search company Google. Google's apparent success
suggests that a new approach to translation -- fancy math rather than
linguistic know-how -- may be the way forward in a field that has
struggled with the nuance and ambiguity of human language.
"Nobody in my team is able to read Chinese characters," says Franz
Och, who heads Google's machine-translation (MT) effort. Yet, they are
producing ever more accurate translations into and out of Chinese --
and several other languages as well.
To demonstrate the software's prowess, Mr. Och displayed an Arabic
newspaper headline at a recent media tour of Google's headquarters in
Mountain View, Calif. One commercially available MT program translated
it: "Alpine white new presence tape registered for coffee confirms
Laden." Then he displayed the translation from Google's prototype,
which made considerably more sense: "The White House Confirmed the
Existence of a New Bin Laden tape."
Of course, every MT program can point to strengths in its approach
versus weakness in others', experts say. The key is whether
statistical systems have become powerful enough to outperform the
intensive, rules-based systems now available.
"These translations were impossible a few years ago," Och says. But
the advent of ever-cheaper and faster data-crunching and the
mushrooming number of online documents have changed the
equation. Google has improved the algorithms for its MT program, he
says, by feeding its computers the equivalent of 1 million books of
text, using sources such as parallel translations of United Nations
Google's MT system is still under development and not available to the
public. Talking about it at an event for journalists and industry
analysts may mean that at least a test version will be coming in the
next few months, observers speculate.
"The results were very impressive, not the stupid machine translation
you see on the Internet, which isn't really good," says Philipp
Lenssen, who's been writing about Google in his online blog, Google
Blogoscoped, since May 2003.
"This opens up a lot of new possibilities because you don't really
want to read machine translation at the moment," Mr. Lenssen says. He
speculates that it could be a perfect part of a Google Web browser,
should the company decide to release one. A user might search the
entire Web in his native language and have pages returned to him
already translated. "You can apply it to so many situations," he says.
Many translations, one root ...
Today, nearly every translation service offered on the Web -- AOL,
Alta Vista, Babblefish, even Google's -- is powered by translation
technology developed by Systran. The company, based in San Diego and
Paris, has been involved in MT for more than 30 years. Each day, it
translates more than 25 million Web pages.
MT involves years of hard work creating rules for translation between
a pair of languages, says Dimitris Sabatakakis, chief executive
officer of Systran. Using statistical methods, such as Google does, is
a well-known technique. "There is no technology breakthrough," he
says. "Everybody does the same."
Machine translations, he says, work best if the original text is
written with care to make it easily translatable, avoiding problematic
or ambiguous words and phrases. More and more websites, especially
those interested in e-commerce, are trying to create text that is
easily translated, Mr. Sabatakakis says. Though machine translations
are often less than perfect, he says, they're still useful to gain a
quick idea of what a website is all about.
Today, Systran offers translations between 40 language pairs, and in
the next 12 months it will add 40 more, he says.
Each of the two approaches to MT -- hand-tailoring rules for translation
between pairs of languages or using statistical analysis to detect
patterns -- has its strengths and weaknesses, says Robert Frederking,
who teaches at the Center for Machine Translation at Carnegie Mellon
University in Pittsburgh.
Rules-based systems are time-consuming to develop and expensive, but
great for specialized tasks, such as translating a manual on
bulldozers, which might have a number of specific and unique terms.
"Systran has put literally hundreds of person years over a 30-year
period into building each language pair that they translate," Dr.
Statistical systems have yet to prove that they can produce superior
translations, says Frederking, who hasn't seen the results of the most
recent NIST evaluations. But doing well at NIST means more than
showing off a few specific examples of better translations to
reporters, he says.
Even evaluating the quality of translations is difficult and
expensive, Frederking says. Since 2002 NIST has used a computer
program called Bleu to do its evaluations. It works "reasonably well,"
Unofficially good ...
The results of the NIST evaluation won't be released until later this
month. "Google did do _very_ well," says Mark Przybocki, the
machine-translation project coordinator at NIST, without confirming
Google's score. Some 20 research groups asked to be evaluated, each
trying new techniques not yet in commercial use. Each group was given
100 news items to translate from Arabic and Chinese into English.
Both rules-based and statistical MT systems can stumble badly on such
generalized reading. One problem is the vast and changing vocabulary.
One analysis of The Wall Street Journal, Frederking says, found that 1
or 2 percent of each edition consists of words that have never before
appeared in the paper. A statistical principle called Zipf's Law holds
that with so many words available, nearly every article will have some
uncommon words, he says. Unless statistical MT programs have seen
these words in many previous contexts, they can mistranslate them.
Proper nouns are a special challenge. Crooner Julio Iglesias, for
example, shouldn't be translated as July Churches, the literal English
translation of his Spanish name. An MT system should be able to spot
which words are names and not translate them, he says. But even that
doesn't help, if the translation is from Japanese or Chinese
characters. "You have to translate them into some kind of Latin
letters," he says.
Frederking predicts that eventually rules-based and statistical
methods will merge, with some knowledge of grammar and syntax being
added to the statistical approach, making for translation programs
that are both broad and deep.
Meanwhile, Google's announcement that it's working on a better MT
system creates interest in the field "and that's a good thing" says
Sabatakakis of Systran. But "we know that there are no magic
solutions. You don't learn a language with statistical methods."
Countries with the most Internet users (in millions):
1. United States: 185.6
2. China: 99.8
3. Japan: 78.1
4. Germany: 41.9
5. India: 37.0
6. Britain: 33.1
7. South Korea: 31.7
8. Italy: 25.5
9. France: 25.5
10. Brazil: 22.3
Source: CIA World Factbook
Copyright 2005 The Christian Science Monitor.
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