AI, Chat GPT: these are the buzzwords of the year. The machine mind has gotten smart enough to score in the 90th percentile on the Bar Exam and capable enough of human mimicry to offer plausible self-help advice. There is of course considerable debate as to what its real impact will be: a complete societal revolution followed by the machines taking over the planet and relegating humans to a subaltern role? Or another over-hyped technology, akin to the self-driving cars that were supposed to have put us all in the passenger seat by now?
But there is at least one arena that seems to keep coming up as an accepted point of agreement: translation. Open AI CEO Sam Altman recently mentioned in an interview that he enjoys being able to travel the word and communicate, thanks to technology, with people anywhere. On my favorite podcast, Vox writer Kelsey Piper said yesterday how excited she is for AI in the field of translation, comparing the transition to the shift in the nineteenth century toward machine-made rugs. Putting aside considerations on the quality of communication when traveling with Google or my thoughts on comparing rugs with language and human interaction (perhaps for another blog post), and acknowledging that despite some of my luddite tendencies I am not actually against the idea of technology used in the service of translation (we will all be cyborgs by the decade’s close), I want to take a moment to express my current reservations about these tools.
Out of curiosity, I’ve tried Chat GPT for translation. And in my professional life, agencies and direct clients often ask me to work with specialized translation tools that use both AI and more traditional machine translation methods. I would say that compared to similar systems even a few years ago, the improvements have been massive. Still, given all the hype around these technologies, I am continually surprised at how extensively I have to intervene, often to the point of completely retranslating giant chunks of text. Just this morning, I was working with one such tool that has been trained in industry-specific French to English translation, and to give you a short example of what we translators are dealing with, here are a few of the suggested translations compared to the end translations:
- “These two models, fitting at the waist” became “These two shoes run small”
- “Think about changing the price tags” became “Be sure to change the price tags”
- “Put the product on the reverse side” became “Turn the garment inside out”
I’ve chosen these three examples nearly at random from a list that would be too long to reproduce here. The main criterion for selection was length: I didn’t want to do an in-depth autopsy of a long passage (again, perhaps for another day), preferring to give you a sample that could be grasped quickly and easily. As you can see, the first translation is simply nonsense. The second makes sense but does not convey the proper thrust of the imperative: this is not a suggestion but a command! Finally, while the third translation almost passes in terms of meaning, it lacks flow and naturalness; it sounds like a machine translation.
Ultimately, my point here is this: not so fast! I think there’s a lot to be considered in terms of what will be lost when we start replacing humans with machines for language jobs, from what it will do to the economy to notions about how we communicate with each other and create shared meaning. But for now, my thoughts are very much in the day-to-day work of translating and editing, and I would say that the task of the translator is still very much translation; that is, analyzing and understanding a text in its source (original) language and then, with considerable care and reflection, carrying that message over to the target language so it can be understood by a “foreign” reader.
This is why I am careful about the kinds of ‘post-editing’ (machine translation followed by human proofreading) jobs I take on. Because the language produced by artificial intelligence and machine translation still requires a lot of work. At times, I even ask myself if it takes me longer than a traditional translation, since I have to consider not only the source text but also the suggested translation–twice the reading! Moreover, the power of suggestion is difficult to counter. If the translation provided by the machine is not good–or simply if it could be better–it takes a certain amount of almost willpower to forget it and come up with something else.
My fear is that the promises of AI in this field will serve to drive down prices, even if the “assistance” provided does not save time in a proportionate way.
pernsteiner translations 