technology that would reach so many people: “The idea of being able to build a real system that millions of people might use to break down the language barrier, that was a very exciting thought.”
Och and the machine-translation team he started to build at Google began with his existing systems for translating Chinese, Arabic, and Russian into English, fanning out over the Web to find as many examples as possible of human-translated texts. The sheer scope of Google’s areas of interest was a help here—it turns out that many of the millions of books Google was busily scanning for its Book Search project have high-quality translations. But another Google invention called MapReduce was even more important. The more statistical associations that a translation system can remember, the faster and more accurately it can translate new text, Och says—so the best systems are those that can hold hundreds of gigabytes of data in memory all at once. MapReduce, the distributed-computing framework that Google engineers developed to parallelize the work of mapping, sorting, and retrieving Web links, turned out to be perfect for operating on translation data. “It was never built with machine translation in mind, but our training infrastructure uses MapReduce at many many places,” says Och. “It helps us, as part of building those gigantic models, to manage very large amounts of data.”
By October of 2007, Och’s team was able to replace the third-party, rules-based translation software Google had been licensing from Systran—the same technology behind AltaVista’s Babel Fish (which now lives at babelfish.yahoo.com)—with its own, wholly statistical models. Today the Web-based Google Translate system works for 58 languages, including at least one—Latin—that isn’t even spoken anymore. “There are not so many Romans out there any more, but there are a lot of Latin books out there,” Och explains. “If you want to organize all the world’s information and make it universally accessible and useful, then the Latin books are certainly part of that.”
And new languages are coming online every month. Human-translated documents are still the starting point—“For languages where those don’t exist, we cannot build systems,” Och says—but over time, the algorithms have gotten smarter, meaning they can produce serviceable translations using less training data. “We have Yiddish, Icelandic, Haitian Creole—a bunch of very small languages for which it’s hard to find those kinds of documents,” Och says.
Today Google Translate turns up in a surprising number of places across the Google universe, starting with plain old search box. You can translate a phrase at Google.com just by typing a phrase like “translate The Moon is made of cheese to French” (the translation offered sounds credible to me: La Lune est faite de fromage). For those times when you know the best search results are likely to be in some other language, there’s the “Translated Search” tool. And when results come up that aren’t in your native language, Google can translate entire Web pages on the fly. (If you’re using Google’s Chrome browser or the Google Toolbar for Internet Explorer or Firefox, page translation happens automatically.) Google can translate your Gmail messages, your Google Talk chats, and your Google Docs. It can even render the captions on foreign-language YouTube videos into your native tongue. And, of course, there are mobile-friendly versions of the Google translation tools, including the Android app.
In September, Hugo Barra, Google’s director of mobile products, hinted that the conversation-mode version of the Google Translate Android app would be available “in a few months,” meaning it could arrive any day now. But whenever it appears, Och and his team aren’t likely to stop there. In fact, they’re already thinking about how to get around some of the barriers that remain between speakers of different languages—even if they have an Android smartphone to mediate between them.
“If I were speaking German now, ideally we should just be able to communicate, but there are some user interface questions and issues,” Och points out. There’s the delay, for one thing—while the Google cloud sends back translations quickly, carrying on a conversation is still an awkward process of speaking, pushing a button, waiting while the other person listens and responds, and so on. Then there’s the tinny computer voice that issues the translation. “It would be my voice going in, but isn’t my voice coming out,” Och says. “So no one knows how [true simultaneous machine translation] would work, until we get to the Babel fish or the Universal Translator from Star Trek—where it just works, and language is never an issue when they go to a different planet.”
Coming in Part 3: A talk with Hartmut Neven, leader of the team that created Google Goggles, the company’s eyebrow-raising computer vision tool.
[Update, 2/28/11: Click here for a convenient single-page version of all three parts of “Inside Google’s Age of Augmented Humanity.”]
