People outside the language industry often tell us - more or less in jest - that our jobs are on the line, and will soon be replaced by machines, anyway. This month’s column is a reflection on exactly that. Feel free to forward it to your friends or family members who just won’t stop teasing you about the rise of the machines.
We’ve all done it - even professional translators and interpreters.
What do you do when you come across an article in a language you don’t speak? Run it through Google Translate. When you need to have a quick chat over video with someone and don’t have a common language? Skype Translator can help. Or that exotic menu written in a foreign language while on vacation? Just open the handy translator app on your smartphone, point it at the text, and let the magic begin.
For many basic tasks, these automatic tools work well enough. They’re certainly not perfect, but do help us grasp the gist of the message and make ourselves understood.
All of this leads us to the million dollar question: Will translators and interpreters be out of a job soon?
We don’t think so. But as machine translation improves, it is becoming useful in many situations that go beyond the basic examples we just mentioned.
Why is this the case? Basically, the answer boils down to two key developments: big data and a new, better approach to machine translation.
First, big data. Strip away the jargon, and you’re left with a key idea: that software can analyze huge amounts of data and detect useful patterns far faster and better than a human ever could. This applies to plenty of things other than language, of course - from real time traffic reports to targeted advertising or Netflix’s recommendations for what you should watch next.
In language technology, companies like Google and DeepL draw on huge bilingual and multilingual corpora. Their databases include millions of sentences from original documents along with their translations produced by humans. These treasure troves of well-translated content form the backbone of modern machine translation.
Second, new machine translation models. Until a few years ago, the statistical approach to machine translation was widely used. In this model, computers identify sequences of words in the original text, look up possible translations, and then use statistical models to decide which translation is most likely to be right.
The new kid on the block, neural machine translation, uses a different approach: Instead of relying on probability for the right translation, artificial intelligence recognizes patterns in the source and the target language and matches the two. These patterns go beyond single words to entire phrases and sentences. And recent studies have shown that this method yields better translations than before.
Yet machine translation has plenty of hurdles to overcome. Whereas human translators understand one sentence in light of an entire text (and more!), machine translation often operates on a sentence-by-sentence basis. Plus, it needs huge amounts of data in the form of human-translated text, which is not equally available for every language pair. Machines still struggle with concepts that were not in their data set, and cannot grasp jokes, irony, cultural references, puns, rhymes, and the other fun stuff that makes communication so rich.
Nevertheless, technological developments are changing the face of the language industry. Many companies already save time and money by having their texts translated by a machine and polished by a professional linguist; the jury’s still out on quality and job satisfaction. Others use controlled language with a smaller subset of words and strict rules. After all, a simple text is easier for a machine to translate.
As for machine interpretation, it’s even harder than machine translation to get right. That’s because machine interpretation has traditionally included three stages: transcribing speech, using machine translation to convert that text into another language, and then using speech synthesis for the spoken output. An error in the first step can be compounded in later phases. And new machine interpreting models that leave out the machine translation “middleman” are still embryonic at best.
Most importantly, machine translation and interpretation are based on the fallacy that language professionals just “translate words”. But we do far more than that. As research shows, interpreters draw on body language, information on screens, word lists, and more to form meaning; we add information, explain cultural references, advise speakers, change language registers when needed, and detect and solve all sorts of problems.
In sum, although machine translation and interpreting can facilitate basic understanding, they still fall far short of what humans can do. So while we encourage you to keep an eye on technological developments - which may help to streamline the work of language professionals - you should still talk to a professional translator or interpreter for your next big project.
PS. Questions or ideas about interpreting technology? Drop us a line at [email protected]! We do the research, so you don’t have to.