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In the first episode of an iconic sci-fi television series decades ago, a NASA test pilot was seriously injured in the crash of an experimental aircraft. The emergency medical team replaced three of Colonel Steve Austin’s four limbs and one eye with nuclear-powered bionic implants, while a voiceover intoned, “We can rebuild him. We have the technology. We can make him better than he was. Better...stronger...faster." During the several seasons of the show, the resulting six-million-dollar-man worked as a secret agent, using his now superhuman powers to battle villains. Since then, science has advanced physical and mental enhancement on many fronts, both to restore normal function for the disabled and to enhance human performance.
Let’s consider language professionals that battle some other villains – translators and interpreters work hard to eliminate linguistic obstacles to understanding essential information in daily life, commerce, politics, health and safety, and every other sphere of human life. The problem is fundamental – there’s so much content generated every day that there’s not enough of these super-heroes to keep pace. Our analysis found that:
It won’t be one or the other, but a combination of the two. Why? There aren’t enough human translators to do it all, and machine translation still isn’t ready for prime time in many applications:
Seven technologies, most of them driven by machine learning, can enhance the capabilities of translators. In 2017 we isolated “augmented translation,” a machine-driven but human-centric approach in which linguists work directly with MT and an array of other technologies that support them, but that leaves them in charge. Based on the innovation of adaptive neural MT, it allowed humans to participate in the real-time, on-line training of neural engines. Rather than position the professional translator in the reactive position of cleaning up after a machine, it put them right in the middle of the training exercise.
Here are the seven language technologies that will enhance human translator performance at the same time they cement the central role of specialists in the process. Each of these langtech offerings augment translator capabilities on their own, but in aggregate improves the content as well. The net effect will be more intelligent source and target content, produced by humans and machines in greater harmony than in today’s post-editing sweatshops.
No current software developer provides all of the components of this vision for augmented translation, and it is unlikely any single company could deliver all of them anytime soon on its own. Nevertheless, CSA Research expects that microservice-based TMSes will allow langtech and content management software vendors to develop products with these augmenting technologies and snap them together into seamless experiences for translators. In concert they will put human specialists where they add the most value in the translation process and make them more efficient – better…stronger…faster.
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