In Google’s vision of the future, people will be able to translate documents instantly into the world’s main languages, with machine logic, not expert linguists, leading the way.
Google’s approach, called statistical(统计的) machine translation, differs from past efforts in which it does without language experts who program grammatical rules and dictionaries into computers. Instead, they feed documents humans have already translated into two languages and then rely on computers to decide patterns for future translations.
While the quality is not perfect, it is an improvement on previous efforts at machine translation, said Franz Och, 35, a German who heads Google’s translation effort at its Mountain View headquarters south of San Francisco. “Some people who have been in machine translations for a long time see our Arabic-English output, and then they say, that’s amazing, that’s a breakthrough.” Said Och. “And then other people who have never seen what machine translation was read through the sentence and they say, the first mistake here in Line Five-it doesn’t seem to work because there is a mistake there.”
But for some tasks, a mostly correct translation may be good enough. Speaking over lunch this week in a Google cafeteria famed for offering free, healthy food, Och showed a translation of an Arabic Web news site into easily digestible English.
Two Google workers speaking Russian at a nearby table said, however, that a translation of a news site from English into their native tongue was understandable but a bit awkward. Och, who speaks German, English and some Italian, feeds hundreds of millions of words from parallel texts such as Arabic and English into the computer, using United Nations and European Union documents as key sources.
Languages without considerable translated texts, such as some African languages, face greater obstacles. “The more data we feed into the system, the better it gets.” said Och, who moved to the United States from Germany in 2002.
The program applies statistical analysis, an approach he hopes will avoid diplomatic embarrassing mistakes in diplomatic situations, such as when Russian leader Putin’s translator annoyed then German Chancellor(总理) Gerhard Schroeder by calling him the German “Fuhrer (“leader” in English),” which is forbidden in that context because of its association with Adolf Hitler.
“I would hope that the language model would say, well, Schroeder is…very rare but Bundeskanzler Gerhard Schroeder is probably 100 times more frequent than Fuhrer and then it would make the right decision.” Och said.
1.What is the best title of this passage?
A.Google Seeks World of Instant Translations.
B.Google Starts a Revolution of Machine Translations.
C.A Breakthrough Makes Google World Brighter.
D.Surf Google and Try the New Computer System.
2.In what way is “Google’s machine translation” different from previous ones?
A.Linguists guide the computer translation on Google.
B.International official papers are programmed as its major sources.
C.Rules and dictionaries are fed into computers to support it.
D.Google daily updates the program of this computer translation.
3.We can learn from the passage that users ___________.
A.think highly of Google’s new approach
B.criticize it for its broken translation
C.hope Google can perfect it before launching
D.hold different opinions towards Google’s new approach
4.Why are there more troubles in translations relating to African languages?
A.Most of the translated materials are not properly translated.
B.Experts managing the program know poorly of African languages.
C.It’s hard to find enough materials of African translation works.
D.The UN and EU fail to provide translated African documents.
5.Statistical analysis in this passage is conducted by ___________.
A.counting how frequently a word is used in the language
B.hiring people who speak different languages
C.using the computer with its own grammatical rules
D.reminding users of the likely embarrassing mistakes