A Survey of Statistical Machine Translation (April 2007)

Adam Lopez

Computational Linguistics and Information Processing Laboratory
Institute for Advanced Computer Studies
Department of Computer Science
University of Maryland
College Park, MD 20742
alopez@cs.umd.edu

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.