Affiliation:
1. University of Edinburgh, Edinburgh, United Kingdom
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 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.
Funder
Office of Naval Research
Defense Advanced Research Projects Agency
Publisher
Association for Computing Machinery (ACM)
Subject
General Computer Science,Theoretical Computer Science
Reference202 articles.
1. Syntax directed translations and the pushdown assembler
2. Al-Onaizan Y. Curin J. Jahr M. Knight K. Lafferty J. Melamed D. Och F. J. Purdy D. Smith N. A. and Yarowsky D. 1999. Statistical machine translation: Tech. rep. Center for Speech and Language Processing Johns Hopkins University. Al-Onaizan Y. Curin J. Jahr M. Knight K. Lafferty J. Melamed D. Och F. J. Purdy D. Smith N. A. and Yarowsky D. 1999. Statistical machine translation: Tech. rep. Center for Speech and Language Processing Johns Hopkins University.
3. Distortion models for statistical machine translation
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