A survey of statistical machine translation

A survey of statistical machine translation (2007)

by Adam Lopez

Citations:

30 - 3 self

Datum	Value	Source
TITLE	A Survey of Statistical Machine Translation	SVM HeaderParse 0.2
AUTHOR NAME	Adam Lopez	SVM HeaderParse 0.2
AUTHOR AFFIL	Computational Linguistics and Information Processing Laboratory; Institute for Advanced Computer Studies; Department of Computer Science; University of Maryland	SVM HeaderParse 0.2
AUTHOR ADDR	College Park, MD 20742	SVM HeaderParse 0.2
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.	SVM HeaderParse 0.2
YEAR	2007	SVM HeaderParse 0.2
CITATIONS	176 found	ParsCit 1.0