How Machine Translation Advances Impact Human Roles

How will technology affect the translator's experience?

Last Updated: May 15, 2019 5:52AM

Depending on your role in the translation and localization world, the Hitchhiker’s Guide Babelfish and Star Trek Universal Translator could represent either a far-off dream or a nightmare. Until last year, though, they certainly didn’t seem like a reality.

Enter the Waverly Labs Pilot and Google’s Pixel earbuds, which offer real-time translations in their wearers’ ears. Constant advances in Machine Translation (MT) make such leaps in instant translations possible—and that impacts everyone in the translation and localization space.

Maturing Machine Translation

By 2016, explained Jay Marciano, Lionbridge Director of Machine Translation, the neural machine translation wave had already rolled through the big online systems. “What we’ve seen over the past two years, and especially in 2018, is the maturing of the tool set,” he said.

Industry experts appear to agree: ever-accelerating technological abilities won’t eliminate the need for professional translators. Instead, technology will elevate the tasks performed by people in the translation process.

What do MT advances mean for humans working in the translation space? Here are three outcomes we expect.

1. Quality Estimation

The evolution of MT has catalyzed an important shift in language services. Now, human involvement has moved from creating translations from scratch to reviewing and editing draft translations created by computers. In this workflow, professional translators can bypass the mundane. Using MT, they can leverage their skills  for more complex pieces that require a human touch for technical, emotional, or tonal reasons. (After all, even Star Trek couldn’t imagine a world in which a computer could crack a joke.)

These quality estimation efforts don’t just improve translation and localization for current customers. They also provide a positive feedback loop that trains MT algorithms on what they got right and wrong. The process is thus designed to increase the corpus of accurate data, so newly trained systems can have a better base for learning.

2. Social Impact Analysis

The “garbage in, garbage out” principle has existed for decades and applies not only to number logic but to attitudes. Varying tests have shown how terrifyingly quickly datasets can turn an artificial intelligence into a young Alfred Hitchcock  or, even more sinister, an algorithm that reinforces racial bias when deciding mortgage rates.

“If you train a system using biased data, you end up with a biased system,” explained Marciano. “Detecting one’s own biases is very difficult for people to master, and it requires a level of social awareness that is well beyond the current capability of computers.”

Fighting sexism in machine translation has become a priority for some of the best-known machine translation systems, including Google. It continues to be the duty of writers, editors, and professional translators to provide minimally biased content and check for bias in the post-edit phase. As machine translation becomes faster and more accurate, human effort can be redirected at data collection, preparation, and review phases.

3. Integration and Scalable Efficiency

As always, Lionbridge continues to push for better, faster translations. To that end, we’ve developed a unique machine translation strategy. We continue to develop flexible, API-driven infrastructure to harness best-in-class MT and fine-tune those results with extensive linguistic expertise.

We constantly strive to produce higher-quality translations for our customers more efficiently. Harnessing always-improving MT allows us to do that. With MT, we can use translation memories to accelerate translations and thus minimize overhead and transaction costs. And, as always, it allows us to leverage experienced professional translators to do what they do best: infuse translations with human nuance and expertise and ensure the translations we provide are of the highest linguistic, cultural, and subject matter quality.

Our cloud-based system lets us localize at a segment level, chunking content and processing it according to our customers’ needs before sharing it with the appropriate Lionbridge worker. The more automation we add to that process, the faster, more accurate, and less costly the experience for our clients. As machine translation continues to advance, the value of cloud platforms like Lionbridge’s will only grow stronger.

Want to learn more about Lionbridge’s cutting-edge and high-quality approach to translation? Reach out today and get started on your next project.

#AI #localization #machine translation #translation

Lion
AUTHOR
April M. Crehan