Andrew Revkin asked what I thought about his arguments for greater development and use of automated language translation technologies. In his piece “The No(w)osphere,” Revkin writes:
As the human population heads toward nine billion and simultaneously becomes ever more interlaced via mobility, commerce and communication links, the potential to shape the human journey — for better or worse — through the sharing of ideas and experiences has never been greater. […]
But language remains a barrier to having a truly global conversation…]
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Automated translation remains clumsy, at best, these days. (One perfect illustration is the website “Translation Party,” which translates an English phrase into Japanese, then translates it back to English, then back to Japanese, until it reaches “equilibrium” — a point where the English and the Japanese auto-translate back and forth precisely.) Linguistic accuracy is a much harder problem than technology pundits of a few decades ago had expected. Nonetheless, as Revkin points out, there are a number of projects out there that suggest that a future of relatively useful automated translation is probably fairly near.
Here’s the twist: I suspect that a less-than-perfect system would be better than an idealized perfect translation. Why? Because an imperfect system would require us to speak more simply and in a more straightforward fashion, with fewer culture-specific idioms and convoluted sentences, as we do today with our current tools. Working with people for whom English is not their primary language, I know that I need to speak and write in a way that doesn’t lend itself to unintended ambiguity or confusion. If I knew that an automated system could be tripped up by overly-complex language, I’d be as careful and precise as possible.
But in everyday conversation, we don’t tend to speak carefully and precisely. Correspondingly, an effectively perfect system would let us slip into the kinds of discussion and writing patterns that we use with other native speakers. I suspect that, counter-intuitively, this would lead to more confusion and friction, as meaning is culturally-rooted. A perfect translation of the denotation of a word or phrase may not carry the correct connotation; moreover, the translated word or phrase may have a very different connotation in a different culture.
In other words, translation technology that offers results that make sense linguistically, and carry the proper surface meaning of the words and phrases used, could well be close at hand. But translation technology that offers results that have the same meaning in both languages, especially with complex or idiomatic phrasing, probably awaits the arrival of relatively strong machine intelligence. Simply put, it would require software that understood what you meant, not just what you said.
We should be careful not to get these two outcomes confused. The more that we expect our translation tools to convert meaning, not just phrasing, the more likely we are to be unhappy with the results.