For many people, their first exposure to Machine Translation (MT) came through Google’s automated engine. For years, Google Translate has been at the forefront of free, widely available machine translation technology. It also has a reputation. Some will (perhaps justifiably) say the results are never good enough for publication. Others, after comparing throughputs from different engines, will conclude that Google’s is among the most efficient machine translation engine. In the world of machine translation, however, efficiency does not equate to publishable quality. If you have content requiring translation and are wondering whether machine translation is suitable, this blog will help you discover which approach to machine translation would best fit your needs.
Translation is not a hard science. There is no single result to your linguistic equation, and while accuracy will always remain one of our primary concerns, the richness of each language allows for a great deal of flexibility. This also means that sometimes, our clients wish to send us corrections.
There is no denying that MT (machine translation) has become a cornerstone of localization as clients demand faster turnaround times, better connectivity, and increased accuracy. MT is becoming another tool in the toolbox for translation services providers.
The recent advent of neural machine translation (NMT) surely leaves some companies wondering how, exactly, does this technology enables LSP (Language Service Providers) to deliver faster, better and more accurate translations. Read on to learn how.
The typical work day of an English to French translator is marked with many linguistic dilemmas. One of those quandaries is whether to translate specialized terms with newly created French equivalents – officially normalized neologisms, if you will – or to leave them in English in the middle of a localized sentence in order to obtain the best translation quality.