Abstract
After several decades of work on rule-based machine translation (MT) where linguists try to manually encode their knowledge about language, the time around 1990 brought a paradigm change towards automatic systems which try to learn how to translate by looking at large collections of high-quality sample translations as produced by professional translators. The first such attempts were called example- or analogy-based translation, and somewhat later the so-called statistical approach to MT was introduced. Both can be subsumed under the label data-driven approaches to MT. It took about 10 years until these self-learning systems became serious competitors of the traditional rule-based systems, and by now some of the most successful MT systems, such as Google Translate and Moses, are based on the statistical approach.
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