Originally posted on LinkedIn by Greg Rosner. I saw the phrase “linguistic janitorial work” in this Deloitte whitepaper on “AI-augmented government, using cognitive technologies to redesign public sector work”, used to describe the drudgery of translation work that so many translators are required to do today through Post-editing of Machine Translation. And then it hit me what’s really going on. The sad reality over the past several years is that many professional linguists, who have decades of particular industry experience, expertise in professional translation and have earned degrees in writing, whose jobs have been reduced to sentence-by-sentence clean-up of translations that flood out of Google Translate or other Machine Translation (MT) systems.
Though machine translation has been around for decades, the most you’ll read about it is the perceived proximity to the mythical “Babel Fish” --an instantaneous personal translation device-- itself ready to replace each and every human translator. The part that gets left out is machine translation's relationship with human translators. For a long time, this relationship was no more complex than post-editing badly translated text, a process most translators find to be a tiresome chore. With the advent of neural machine translation, however, machine translation is not just something that creates more tedious work for translators. It is now a partner to them, making them faster and their output more accurate.