THOSE passingly familiar with machine translation (MT) may well have reacted in the following ways at some point. “Great!” would be one such, on plugging something into the best-known public and free version, Google Translate, and watching the translation appear milliseconds later. “Wait a second…” might be the next, from those who know both languages. Google Translate, like all MT systems, can make mistakes, from the subtle to the the hilarious.

The internet is filled (here for example) with signs badly machine translated from Chinese into English. What monolingual English-speakers don’t realise is just how many funny mistakes get made in translating the other way. Take, for example, the Occupy Wall Street protester in 2011 who seems to have plugged “No more corruption” into a computer translator and made a sign with the resulting Chinese output. It read: “There is no corruption”.

MT is hard. It has occupied the minds of a lot of smart people for decades, which is why it is still known by a 1950s-style moniker rather than “computer translation”. Older models tended to try to break down the grammar or meaning of the source text, and reconstruct it in the target language. This was so difficult, though, that in retrospect it is unsurprising that this approach started running into intractable problems. But now, in an early application of “big data” (before the phrase became vogue), MT systems typically work statistically. If you feed a lot of high-quality human-translated texts into a translation model in both target and source languages, the model can learn the likelihood that “X” in language A will be translated as “Y” in language B. (And how often, and in what contexts, “X” is more likely to be translated as “Z” instead.) The more data you feed in, the better the model’s statistical guesses get. This is why Google (which has nothing if not lots of data) has got rather decent at MT. More.

See: Johnson | The Economist

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