Coming to terms with machine translation

 ‘Human-engineered’ translation is just as inadequate in more important domains. In our courts and hospitals, in the military and security services, underpaid and overworked translators make muddles out of millions of vital interactions. Machine translation can certainly help in these cases. Its legendary bloopers are often no worse than the errors made by hard-pressed humans.

Machine translation has proved helpful in more urgent situations as well. When Haiti was devastated by an earthquake in January, aid teams poured in to the shattered island, speaking dozens of languages — but not Haitian Creole. How could a trapped survivor with a cellphone get usable information to rescuers? If he had to wait for a Chinese or Turkish or an English interpreter to turn up he might be dead before being understood.

Carnegie Mellon University instantly released its Haitian Creole spoken and text data, and a network of volunteer developers produced a rough-and-ready machine translation system for Haitian Creole in little more than a long weekend. It didn’t produce prose of great beauty. But it worked.

The debate

The advantages and disadvantages of machine translation have been the subject of increasing debate among human translators lately because of the growing strides made in the last year by the newest major entrant in the field, Google Translate. But this debate actually began with the birth of machine translation itself.

Linguists, like Noam Chomsky, view a language as a lexicon and a grammar, able to generate infinitely many different sentences out of a finite set of rules. But as anti-Chomsky linguists at Oxford commented, there are also infinitely many motor cars that can come out of a British auto plant, each one having something different wrong with it. Over the next four decades, machine translation achieved many useful results, but, like the British auto industry, it fell far short of the hopes of the 1950s.

Now we have a beast of a different kind. Google Translate is a statistical machine translation system, which means that it doesn’t try to unpick or understand anything. Instead of taking a sentence to pieces and then rebuilding it in the ‘target’ tongue as the older machine translators do, Google Translate looks for similar sentences in already translated texts somewhere out there on the web. Having found the most likely existing match through an incredibly clever and speedy statistical reckoning device, Google Translate coughs it up, raw or, if necessary, lightly cooked.

Google Translate, which can so far handle 52 languages, sidesteps the linguists’ theoretical question of what language is and how it works in the human brain. In practice, languages are used to say the same things over and over again. For maybe 95 per cent of all utterances, Google’s electronic magpie is a fabulous tool. But there are two important limitations that users of this or any other statistical machine translation system need to understand.

The variable quality of Google Translate in the different language pairings available is due in large part to the disparity in the quantities of human-engineered translations between those languages on the web.

But what of real writing? Google Translate can work apparent miracles because it has access to the world library of Google Books. That’s presumably why, when asked to translate a famous phrase about love from ‘Les Miserables’ — “On n’a pas d’autre perle a trouver dans les plis tenebreux de la vie” — Google Translate comes up with a very creditable “There is no other pearl to be found in the dark folds of life,” which just happens to be identical to one of the many published translations of that great novel.

And the programme is very patchy. The opening sentence of Proust’s “In Search of Lost Time” comes out as an ungrammatical “Long time I went to bed early”, and the results for most other modern classics are just as unusable.