Machine translation has come on strong in recent years, with myriad online tools and apps serving as decent alternatives to the arduous process of, well, learning a new language yourself.
A few months back, Microsoft claimed a “historic milestone” when it said it was able to leverage artificial intelligence (AI) to match human performance levels in translating news from Chinese to English. And Google brought its neural machine translation-based (NMT) translation smarts offline — Google Translate users on Android and iOS can now access high-quality translations in 59 languages sans internet.
But as good as machine translation tools are getting, many factors can collude to make relying on machine translation alone a bad move. Overly technical subject matter, unusual language pairings, and awkward source material formatting are among the elements that mean businesses — or anyone with mission-critical translation needs — are often better served by a computer-assisted translation (CAT) approach.
Humans may have to assist with many stages of the translation process, including managing documents, preparing text into a format that’s ready for translation, and — of course — translating and proofreading material. Companies will often have large segments of text, whether individual words, phrases, or paragraphs, that have previously been translated — so rather than doing all that work again, a translation memory database can ensure the translator only needs to work on new text. Naturally, this database improves and expands over time, so the more you use a particular system, the…