But these aspects shouldn’t be taken in isolation from each other. Training an MT engine is a continuous process, and the more high-quality input is provided, the better the results are. Having a good customer feedback loop means that Zendesk is able to target pages that require human intervention. Post-editors contribute to the training process by correcting errors, and the results are fed back into the system. This in turn helps make the content of machine-translated pages better.
It’s a continuous cycle of improvement on all fronts, one which Zendesk seems to have mastered.
The last point is simple, although quite easy to overlook, so it needs to be said again: Today, machine translation is capable of generating usable content.
It’s a common refrain, that’s true, but machine translation uruguay mobile database engines really have come a long way. The days when MT was a novelty with amusing errors is long gone; the quality of machine translations has gone up enough to be useful in industrial and commercial contexts today.
The key difference between then and now boils down to one thing: neural machine translation (NMT). Neural machine translation is a form of MT that uses neural networks, which can process massive amounts of translation data with relative efficiency, increasing the quality of translations exponentially. Today, all MT engines use neural machine translation.
An MT engine that is well-trained in translating for a specific sector is capable of producing information that can generally be understood. In the context of the case at hand—localizing support pages—this is precisely the point.
The language on a support page doesn’t need to have the polish and style of, say, ad copy, after all. It simply needs to be understandable enough for users to know what to do without having to open a ticket for human assistance. MT today is more than capable of doing this, compared to the past decade, or even the past five years.