Machine translation (MT) is a form of translation where a computer program analyses the text in one language - the "source text" - and then attempts to produce another, equivalent text in another language - the target text - without human intervention.
Currently the state of machine translation is such that it involves some human intervention, as it requires a pre-editing and a post-editing phase. Note that in machine translation, the translator supports the machine and not the other way around.
Nowadays most machine translation systems produce what is called a "gisting translation" - a rough translation that gives the "gist" of the source text, but is not otherwise usable.
However, in fields with highly limited ranges of vocabulary and simple sentence structure, for example weather reports, machine translation can deliver useful results.
Machine translation (MT) is the application of computers to the task of translating texts from one natural language to another. One of the very earliest pursuits in computer science, MT has proved to be an elusive goal, but today a number of systems are available which produce output which, if not perfect, is of sufficient quality to be useful in a number of specific domains.
Although the two concepts are similar, machine translation (MT) should not be confused with computer-assisted translation (CAT) (also known as machine-assisted translation (MAT)).
In machine translation, the translator supports the machine, that is to say that the computer or program translates the text, which is then edited by the translator, whereas in computer-assisted translation, the computer program supports the translator, who translates the text himself, making all the essential decisions involved.
It is often argued that the success of machine translation requires the problem of natural language understanding to be solved first.
Given enough data, most machine translation programs work well enough for a native speaker of one language to get the approximate meaning of what is written by the other native speaker (i.e. producing a "gisting translation"). The difficulty is getting enough data of the right kind to support the particular method