What’s Lost (And Found) In Machine Translation
What’s the Big Idea?
A few milestones in the short but storied history of machine translation: in 1939, Bell Labs presented the first speech synethesizing device, the Voder, at the World’s Fair in New York. In 1978, the first spoken words were transmitted across the Internet. June 2012 saw the release of VoiceTra4U-M, an iPhone app developed by the global Universal Speech Translation Advanced Research Consortium (U-STAR) which enables voice translation of 13 different languages.
Today’s translation machines, both written and spoken, “are extremely clever and give us a lot reasons for thought about what language is and how we may understand language better, but the way they work bears little resemblance, in fact, no resemblance at all to the way human beings both speak,” says David Bellos, a translator and director of the Program in Translation and Intercultural Communication at Princeton University.
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Computers decode and reproduce spoken human language in much the same way they translate written language — by effectively transcribing the speech in the source language into text and putting it through a translation device which “sounds out” the text, “just like your telephone answering device does.” (This feature is used and will, says Bellos, always be used in machines that simulate speech translation.) Software translation programs like Google’s, Yahoo’s, and Microsoft’s are essentially statistical engines. Programmers use data to train their algorithms on human-translated parallel texts so that they automatically “learn” how to translate.
Over the years, the technology has become more sophisticated, but speaking to an automated voice on the other end of the line is still an exercise in frustration. The results of programs like Google Translate are notoriously comical. Here, for instance, is Hamlet’s famous “To be or not to be” soliliquy translated from the original English to Chinese, back to English again via Google Translate:
Or not, this is a problem:
Whether this is a noble mind suffer
Outrageous slings and arrows of Fortune
Or take up arms against a sea of troubles,
And opposing the closure, after they die, to sleep
A sleep to say we end
The heart of pain, as well as countless other natural shocks
This flesh is heir to it?
As Phil Blunsom, a researcher at Oxford University, told the BBC, “the time when a computer can match the interpretive skills of a professional is ‘still a long way off.'”
What’s the Significance?
The limitations of machine translation are indicative of the broader historical limitations of symbolic A.I. Early researchers regarded both the human brain and human language as systems of explicit rules which could be pinned down, catalogued, and unlocked — but despite a few breakthroughs in the field, we’ve still not come close to building a brain or decoding the nuances of language. Perhaps the problem is more than technological. Perhaps it is unsolvable.
Why? “You possess a skill that hardly any computer programme does,” explains the author of a 2009 paper from the University of Copenhagen. In studies, people are able to pick up on subtle distinctions in the meanings of words that computer systems always miss, for example:
(1.1) (a) The command interface defines a single method called “execute” that is invoked by the
internal CommandExecutor when a command is to be executed.
(b) An Iranian cleric, Hojatoleslam Rahimian, called today for the leaders of Iran’s
opposition Green Movement to be executed.
According to Bellos, machine translation will always require the existence of human translators. Google Translate and the automated phone operator fall flat when they try to understand passages that contain complexity and variation — abstract ideas, shifts in tone, words that mean more than one thing.
Still, he says, machine translation has great potential to expand our sense of the possibilities of communications, as civilization grows increasingly global. “The way airplanes fly resembles not at all the way birds fly. It doesn’t have to. What you want is the flight.”
The overall picture is this. The more machine translation there is the more translation will happen, the more people will expect to be able to communicate with other folk and the more they will realize that although machines can clear the ground the actual translation has to be done by somebody because language is human behavior. It’s machine simulated, but they’re not doing anything like what a human translator is doing.
Image courtesy of Shutterstock.
