Fancy math takes on je ne sais quoi

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. Frederking says.

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," he says.

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."

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

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