LAMP-TR-135 April 2007 CS-TR-4831 UMIACS-TR-2006-47 A Survey of Statistical Machine Translation Adam Lopez Computational Linguistics and Information Processing Laboratory Institute for Advanced Computer Studies Department of Computer Science University of Maryland College Park, MD 20742
[email protected] 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. This is a revised draft of a paper currently under review. The contents may change in later drafts. Please send any comments, questions, or corrections to
[email protected]. Feel free to cite as University of Maryland technical report UMIACS-TR-2006-47. The support of this research by the GALE program of the Defense Advanced Research Projects Agency, Contract No. HR0011-06-2-0001, ONR MURI Contract FCPO.810548265, and Department of Defense contract RD-02-5700 is acknowledged. Contents 1 Introduction 1 1.1 Background and Context .