Gideon Lewis-Kraus on Machine Translation: 'All texts have some purpose in mind'

At the New York Times Magazine, writer Gideon Lewis-Kraus raises some interesting questions around machine translation--from WWII crytopgraphy to algorithms used by Google and Skype Translator to ideas of infidelity. "Translation promises unity but entails betrayal," he writes. And is it an art or a math problem? More:

Though some researchers still endeavor to train their computers to translate Dante with panache, the brute-force method seems likely to remain ascendant. This statistical strategy, which supports Google Translate and Skype Translator and any other contemporary system, has undergone nearly three decades of steady refinement. The problems of semantic ambiguity have been lessened — by paying pretty much no attention whatsoever to semantics. The English word “bank,” to use one frequent example, can mean either “financial institution” or “side of a river,” but these are two distinct words in French. When should it be translated as “banque,” when as “rive”? A probabilistic model will have the computer examine a few of the other words nearby. If your sentence elsewhere contains the words “money” or “robbery,” the proper translation is probably “banque.” (This doesn’t work in every instance, of course — a machine might still have a hard time with the relatively simple sentence “A Parisian has to have a lot of money to live on the Left Bank.”) Furthermore, if you have a good probabilistic model of what standard sentences in a language do and don’t look like, you know that the French equivalent of “The box is in the ink-­filled writing implement” is encountered approximately never.

Contemporary emphasis is thus not on finding better ways to reflect the wealth or intricacy of the source language but on using language models to smooth over garbled output. A good metaphor for the act of translation is akin to the attempt to answer the question “What player in basketball corresponds to the quarterback?” Current researchers believe that you don’t really need to know much about football to answer this question; you just need to make sure that the people who have been drafted to play basketball understand the game’s rules. In other words, knowledge of any given source language — and the universal cultural encyclopedia casually encoded within it — is growing ever more irrelevant.

Many computational linguists continue to claim that, after all, they are interested only in “the gist” and that their duty is to find inexpensive and fast ways of trucking the gist across languages. But they have effectively arrogated to themselves the power to draw a bright line where “the gist” ends and “style” begins. Human translators think it’s not so simple. The machinist’s attitude is that when someone’s mother is trapped under a house, it’s fussy and self-­important to worry too much about nuance. They see the redundancy and allusiveness of natural languages as a matter not of intricacy but of confusion and inefficiency. Most valuable utterances revert to the mean of statistical probability. If this makes them unpopular with poets and fanciers of language, so be it. “Go to the American Translators Association convention,” one marathon attendee told me, “and you’ll see — they hate us.”

This is to some extent true. As the translator Susan Bernofsky put it to me, “They create the impression that translation is not an art.”

Read it all at the New York Times Magazine.