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A survey of statistical machine translation (2007)
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@TECHREPORT{Lopez07asurvey,
author = {Adam Lopez},
title = { A survey of statistical machine translation},
institution = {},
year = {2007}
}
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Abstract
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 many popular techniques have only emerged within the last few years. This survey presents a tutorial overview of state-of-the-art SMT at the beginning of 2007. We begin with the context of the current research, and then move to a formal problem description and an overview of the four main subproblems: translational equivalence modeling, mathematical 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 notes on future directions.
Keyphrases
statistical machine translation machine learning problem formal problem description future direction parameter estimation state-of-the-art smt many popular technique different approach translational equivalence modeling current research tremendous stride tutorial overview many sample main subproblems mathematical modeling last year natural language human-produced translation
