Statistical Machine Translation

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Statistical machine translation (SMT) treats the translation of natural language as a machine learning problem. By examining many samples of human-produced translation, SMT algorithms automatically learn how to translate. SMT has made tremendous strides in less than two decades, and new ideas are constantly introduced. This survey presents a tutorial overview of the state of the art. We describe the context of the current research and then move to a formal problem description and an overview of the main subproblems: translation modeling, parameter estimation, and decoding. Along the way, we present a taxonomy of some different approaches within these areas. We conclude with an overview of evaluation and a discussion of future directions.
Original languageEnglish
Article number8
Pages (from-to)1-49
Number of pages49
JournalACM Computing Surveys
Issue number3
Publication statusPublished - 1 Aug 2008

Keywords / Materials (for Non-textual outputs)

  • Natural language processing, machine translation


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