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Statistical machine translation (SMT) is a machine translation paradigm where translations are generated on the basis of statistical models whose parameters are derived from the analysis of bilingual text corpora. The statistical approach contrasts with the rule-based approaches to machine translation as well as with example-based machine translation.
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Computer-assisted translation, computer-aided translation, or CAT is a form of translation wherein a human translator translates texts using computer software designed to support and facilitate the translation process.
Computer-assisted translation is sometimes called machine-assisted, or machine-aided, translation.
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The Example-based machine translation (EBMT) approach to machine translation is often characterized by its use of a bilingual corpus with parallel texts as its main knowledge base, at run-time. It is essentially a translation by analogy and can be viewed as an implementation of case-based reasoning approach of machine learning.
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The rule-based machine translation paradigm includes transfer-based machine translation, interlingual machine translation and dictionary-based machine translation paradigms. The RBMT typically uses formal models of our knowledge of language, linguistic intuition of developers.
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Transfer-based machine translation is a type of machine translation, it is based on the idea of interlingua and is currently one of the most widely used methods of machine translation