The progress and potential of machine translation have been much debated through its history. With the assistance of these techniques, MT has proven useful as a tool to assist human translators and, in a very limited number of cases, can even produce output that can be used as is (e.g., weather reports). Improved output quality can also be achieved by human intervention: for example, some systems are able to translate more accurately if the user has unambiguously identified which words in the text are proper names. It follows that machine translation of government and legal documents more readily produces usable output than machine translation of conversation or less standardised text. This technique is particularly effective in domains where formal or formulaic language is used. Ĭurrent machine translation software often allows for customization by domain or profession (such as weather reports), improving output by limiting the scope of allowable substitutions. Solving this problem with corpus statistical and neural techniques is a rapidly growing field that is leading to better translations, handling differences in linguistic typology, translation of idioms, and the isolation of anomalies. Not all words in one language have equivalent words in another language, and many words have more than one meaning. On a basic level, MT performs mechanical substitution of words in one language for words in another, but that alone rarely produces a good translation because recognition of whole phrases and their closest counterparts in the target language is needed.

Machine translation, sometimes referred to by the abbreviation MT (not to be confused with computer-aided translation, machine-aided human translation or interactive translation), is a sub-field of computational linguistics that investigates the use of software to translate text or speech from one language to another.

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