Machine translation (MT)

Machine translation (MT) refers to a content translation method that uses algorithms and machine learning models to translate natural language text. The algorithm breaks down text into words and phrases that are translated into the target language. There are three different types of machine translation: rules-based machine translation, statistical machine translation and neural machine translation.

It is important to mention that not all content types are suitable for MT. MT works best with structured content and unambiguous content, like a user manual or a clinical evaluation report (CER). Machine translation alone cannot accurately translate creative content like novels or marketing content – as it cannot capture the nuances of such content types. Another significant mention is that it’s best to have a human-in-the-loop approach when using MT – meaning that a human translator is involved in checking the accuracy of the translation in the post-editing phase.

Example use cases

• User-generated content such as reviews

• Multilingual technical files for medical devices

• Complex multi-regional user manuals

• eDiscovery or other reasons for parsing large sets of multilingual content

Key benefits

• Enable higher volumes of translation, faster

• Drastically reduce translation costs

• Translate into more languages with the same localization budget