Introduction
As organizations and individuals increasingly need to collaborate with people in other cultures and geographic locations, the removal of language barriers has become a significant problem.
One solution is to find bilingual, or even multilingual, people to translate between languages. However the scarcity of such skills, and the number of possible language combinations can make this approach difficult to scale. Increasingly, automated translation, sometimes known as machine translation, is being employed to solve this problem.
Literal and semantic translation
Early attempts at machine translation applied literal translations. A literal translation is where each word is translated to the corresponding word in the target language. This approach presents some issues. For one case, there may not be an equivalent word in the target language. Another case is where literal translation can change the meaning of the phrase or not get the context correct.
Artificial intelligence systems must be able to understand, not only the words, but also the semantic context in which they are used. In this way, the service can return a more accurate translation of the input phrase or phrases. The grammar rules, formal versus informal, and colloquialisms all need to be considered.
Text and speech translation
Text translation can be used to translate documents from one language to another, translate email communications that come from foreign governments, and even provide the ability to translate web pages on the Internet. Many times you will see a Translate option for posts on social media sites, or the Bing search engine can offer to translate entire web pages that are returned in search results.
Speech translation is used to translate between spoken languages, sometimes directly (speech-to-speech translation) and sometimes by translating to an intermediary text format (speech-to-text translation).