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Apertium is a shallow-transfer machine translation system, which uses finite state transducers for all of its lexical transformations, and hidden Markov models and/or constraint grammars for part-of-speech tagging or word category disambiguation.

Other machine translation systems available at present are mostly commercial and use proprietary technologies, which makes them very hard to adapt to new usages; furthermore, they use different technologies for different language pairs, which makes it very difficult, for instance, to integrate them into a single multilingual content management system. Finally, most of them are not available for most of the languages in the world, as they rely heavily on resources that are not available for any but a few languages.

Apertium uses a language-independent specification to allow for the ease of contributing to Apertium, more efficient development, and enhancing the project's overall growth.

At present, Apertium has released more than 40 stable language pairs, delivering fast translation with reasonably intelligible or excellent results, depending on the language pair. Being an open-source project, Apertium provides tools for potential developers to build their own language pair and contribute to the project.