An MT engine trained on data from the manufacturing sector, for example, would be familiar with the terms used in that industry, but would not be well-suited for, say, machine translations for military and defense. And an MT engine trained on generic data tends to perform less well for any industrial purpose than one that has been specifically trained. That’s why you don’t see companies using Google Translate.
The results are clear for Zendesk. Drain mentions a case in which a localization department approached her for help with improving their translation quality. They, too, used an MT engine, but the results were far from satisfactory. Apart from other factors, such as post-editing and customer feedback, what was made clear is that the MT engine used by Drain and her team was better trained.
But these aspects shouldn’t be taken in isolation from dominican republic mobile database 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.
Creation of usable, actionable content
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.