A survey of Data Driven Machine Translation

Machine Translation (MT) refers to the use of computers for translating automatically from one language to another. The differences between source and target languages and the inherent ambiguity of the source language itself make MT a very difficult problem. Traditional approaches to MT have relied on humans giving linguistic knowledge in the form of rules to transform text. Given the vastness of language, this is a highly knowledge intensive task. Corpus-based approaches to Machine Translation (MT) dominate the MT research field today, with Example-Based MT (EBMT) and Statistical MT (SMT) representing two different frameworks within the data-driven paradigm. Example Based MT is a radically different approach that involves matching of examples from large amounts of training data followed by adaptation and recombination. This survey provides an overview of MT techniques, and covers some of the realted work in Example Based and Statistical approaches to machine translation from 1984 to 2011. The report concludes with a brief discussion on example-based hybrid techniques, existing MT systems and MT evaluation criteria.

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