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Is Neural Machine Translation Ready for Marketing Content?

3 minute read
Terena Bell avatar
With advances in machine learning and neural machine translation marketers may be wondering if this technology is ready to drive marketing initiatives.

When Google Translate first hit the market, it wasn’t very good. Music fans were the first to prove this by making a laughingstock of the app by loading lyrics from songs like Will Smith’s “Fresh Prince of Bel-Air” and the theme song from Moana to see what funny or ridiculous translations Google would generate. While the tool isn’t nearly as bad as videos make it out to be, this negative PR has kept companies from using it. After all, if Google can’t translate song lyrics correctly, why would you trust it with marketing content?

But Google Translate doesn’t represent all machine translation. However, it is a brand that happens to be well-known and free. And, in translation — just like everything else — you get what you pay for.

Related Article: Tips for Building a Content Translation Strategy 

Machine Translation Post-Editing, MT+PE

Professional (not free) machine translation helps companies translate more content more quickly and at a lower price point. And when a human reviewer checks the work, quality is found to be just as high as that of traditional translation. The language industry calls this pairing: machine translation post-editing, or MT+PE for short. Traditionally, when you buy translation, your content goes through two rounds — the first person converts it into the new language then the second checks for error. With MT+PE, the computer takes the first pass, speeding up the entire process. This is what makes machine translation okay for certain marketing material, but not anything too highly-nuanced, Rick Antezana with the Association of Language Companies cautions. “Paid machine translation should be used when translating content of high volume and low risk/low importance,” he says.

Learning Opportunities

It all depends on the training. Words are stored in what computer programmers call an engine. Machine translation engines must be specifically trained for the old and new languages, as well as the subject matter you need. As with all artificial intelligence or machine learning, machine translation needs data to improve. If your company’s tech team uses the engine to translate user questions, it’ll be good at support language but not marketing. And just because an engine’s trained in the language you need doesn’t mean it can translate in the right direction: Spanish into English has traditionally required a different engine than English into Spanish.

Enter Neural Machine Translation

But that’s changing. Laura Brandon, former director of the Globalization and Localization Association, says, “The big development these days is neural machine translation, which is blowing other machine translation out of the water.” Using neural network technology — a type of machine learning designed to mimic neurons in the human brain, neural machine translation can train in multiple languages and directions at once. Separate engines are no longer needed, saving your company precious training time.

So is neural network technology better at translating marketing content? Not necessarily. “When representing any kind of brand — whether it be an enterprise-level, global company or a small software company — using machine translation to represent any kind of content, including marketing content like social is a big gamble, as the software is incredibly well developed, but never perfect,” Antezana says. “The fewer eyeballs on potential content for translation and the higher the volume, the more appropriate it would be.”