To improve accuracy, some translation efforts, including SpeachGear’s, are merging statistical approaches with rule-based systems. “Statistical systems have a tough time with ambiguity, as in ‘He was safe at home,’ ” Palmquist says. “A rule-based system can more easily be told to check if the context is a baseball game.” Rule-based systems also take a lot less computing power and data storage. Google’s service works only if you have a good data connection, because it’s zipping every word back and forth between your phone and the Google servers on Mount Olympus, which do all the heavy lifting.
“You need network access to gigabytes of data for statistical systems,” says Mireille Boutin, a professor of engineering at Purdue University. “But what if you’re traveling and don’t have network access?”
A group led by Boutin is addressing that rather sharp limitation with a translator that runs entirely on a cell phone, no connection needed. To keep the system compact, it is designed specifically to translate conversation relating to ordering in a restaurant. That is harder than it may sound, given that dishes often don’t translate well. For instance, “the ingredients for paella vary depending on the country, the region, and the season,” Boutin notes. “It can take 10 words to translate a dish that has a one-word name in another language.”
But with data connections getting better all the time, the statistical approach is gaining favor, and a number of schemes are being rolled out to improve statistical techniques. One company is setting up a system to tap into the translational skills of the suckers who do stuff online for little or no money, or as leading-edge businesses are careful to call it, crowdsourcing.
Alex Buran, CEO of Translation Services USA in New York, got the brainstorm a year ago to invite people visiting his corporate website to edit or approve small chunks of text processed by the company’s machine-translation engine. If two or more other visitors approve one of those chunks, it enters the engine’s database as the new preferred translation. The engine gets better, and the winning translators get an award of 10 cents or more. (Average income for serial translators is about $11.50 an hour.) “We believe we’ll make the machine results so accurate that it will displace human translators in the end,” Buran says, adding that some 7,500 people are hard at it today.
Still, the gap between machine translators and their human counterparts may never be completely closed. “These programs can’t read body language or tone of voice, or deal with new colloquialisms or unusual variations in dialect,” says Craig Schlenoff, a mechanical engineer at the National Institute of Standards and Technology who has evaluated translation software used by U.S. soldiers in Afghanistan for “knock and talks”—visits to local residents for gathering intelligence and improving relations. And Bowen Zhou, who heads the speech-to-speech translation research group at IBM, points out that there are situations demanding subtleties of translation unlikely to be mastered by a machine anytime soon. “Speeches by heads of state require a specific play of words to carry the intended emotion and message,” he says. “And legal documents contain careful constructions with specifically tailored meanings.”
Personally, I’m relieved to hear it. Because that means my imperfect translating cell phone really is just like the Star Trek version. As Kirk himself said of the Universal Translator: “Not 100 percent efficient, of course. But nothing ever is.”
David H. Freedman is a freelance
journalist, author, and longtime contributor to DISCOVER.
You can follow him on Twitter at @dhfreedman.