Clearly defined guidelines help post-editors understand the specific requirements of each project, maintain uniformity in translations, and meet client expectations. By providing comprehensive instructions, you can ensure that all translations align with the desired tone, terminology preferences, and overall quality standards, resulting in more accurate and reliable translations.
Once you’ve chosen your MT engine, the next step is to customize it. But you might think that today’s MT engines are trained upon billions and billions of translated text segments; would your data even make a dent in that kind of training?
The answer is yes. MT engines don’t rely on quantity alone. The albania mobile database quality of data is also important. Today’s MT engines use AI and deep neural networks to make sophisticated connections that depend on high-quality data, which provides the necessary context for the MT engine to make the right translation. Sources of high-quality data include glossaries, translation memories, and text segments that have already been translated properly.
Results may differ among MT engine providers, and some may require more data than others to come up with a well-trained custom MT engine. But with high-quality data, it’s possible to see good results.
Hiring a machine translation post-editor
Once you’re done with your MT engine, it’s time to turn toward finding the right language professional to work with it.
While post-editing work is mostly proofreading and revising MTPE output in one language, it’s not a good idea to give the work to someone who’s monolingual, or unfamiliar with the source language. Machine translation still isn’t perfect, so there may still be errors that only a human translator is able to catch.