With Event Technology, Neural Machine Translation and Remote Simultaneous Interpreting are all vying for publicity, we would be forgiven or thinking that the only choice is between jumping on the high-tech bandwagon and living in a shack on the plains. Many authorities have pleaded for all children to learn to code. The logic is simple, you are either learning how to handle data or you are just part of the data. But might there be a flaw in that logic?
Why Tech Fails
As much as the innovators would never admit it to their angel investors, history is littered with tech that went nowhere. To the well-known flops of laserdiscs and personal jetpacks can be added the expensive failures of nuclear-powered trains and boats and the hundreds of “instant translation” websites that promised to leverage the “power of bilinguals”. Just because a tech exists, that doesn’t mean it will actually make a meaningful difference, just ask the inventor of the gyrocopter.
While the stories of some technologies are unpredictable, there are others where it was clear that there as too wide a gap between what the engineers could do and what the market actually would accept. Take nuclear powered ships. While nuclear submarines are an important part of many navies, the reticence of many ports to let a ship carrying several kilos of activated uranium dock (never mind refuel or take on supplies) spelled the end of that particular dream.
Other times, technology has flopped due to a simple failure to understand the dimensions of the problem. Take those “instant translation” or “interpreting on the go” websites. Almost always the brainchildren of monolinguals who have a severe case of phrasebook-aversion, they all crash and burn when the founders realise that “bilingual” is a very loose concept and those with actual interpreting expertise are highly unlikely to want to spend their time saying the Hungarian for “where is the toilet?” or the Spanish for “I have a headache and can’t take ibuprofen” forty times a day.
The Problem with Machine Translation
To this motley crew, it seems that we have to add more than a few denizens of modern machine translation. With some leading experts busy telling us that translation is just another “sequence to sequence problem” and large software houses claiming that managing to outdo untrained bilinguals is the same as reaching “human parity” (read that article for the truth behind Microsoft’s claim), it is becoming plain that the actual nature of translation is eluding them.
The most common measure of machine translation performance, the BLEU score, simply measures the extent to which a given translation looks like a reference text. The fact that these evaluations and those performance by humans on machine translation texts are always done without any reference to any real-life context should make professional translators breathe more easily.
Only someone who slept through translation theory class and has never actually had a paid translation project would be happy with seeing translation as just a sequence to sequence problem. On the most basic, oversimplified level, we could say that translators take a a text in one language and turn it into a text in another. But that misses the point that every translation is produced for an audience, to serve a purpose, under a set of constraints.
The ultimate measure of translation quality is not its resemblance to any other text but the extent to which it achieved its purpose. If we really want to know how good machines are at translation, let’s see how they do at producing texts that sell goods, allow correct medical treatment, persuade readers, inform users, and rouse emotion without any human going over their texts afterwards to sort out their mistakes.
Skills to Learn before you Learn to Code
All this shows is that there are key skills that you need to learn before you are set loose on coding apps and building social media websites. Before kids code, let them learn to listen so they can hear what the actual problem is. Before they form algorithms, let them learn how to analyse arguments. Before they can call standard libraries, let them learn to think critically. Let them learn and understand why people skills have to underpin their C skills and why asking questions is more important that creating a system that spoon-feeds you the answer.
I hope that, for our current generation of tech innovators, it isn’t too late. We absolutely need technology to improve but we also need there to be more ways for tech innovators to listen to what everyone else is saying. We could do with some disruption in how events are organised and run but the people doing it need to understand the reasons behind what we do now. Interpreting could do with a tech revolution but the tech people have to let interpreters, interpreting users and interpreting buyers sit in the driving seat.
If our time isn’t to be wasted with more equivalents of nuclear-powered trains, if we are to avoid Cambridge Analytica redux, we need monster coders and incredible listeners, innovators who are also thinkers, writers and macro ninjas. It may well be that one person cannot be both a tech genius and a social scientist but we need a world in which both are valued and both value each other.
It’s a world we can only build together.