Paper Info
Title
Syntax based reordering with automatically derived rules for improved statistical machine translation
Abstract
Syntax based reordering has been shown to be an effective way of handling word order differences between source and target languages in Statistical Machine Translation (SMT) systems. We present a simple, automatic method to learn rules that reorder source sentences to more closely match the target language word order using only a source side parse tree and automatically generated alignments. The resulting rules are applied to source language inputs as a pre-processing step and demonstrate significant improvements in SMT systems across a variety of languages pairs including English to Hindi, English to Spanish and English to French as measured on a variety of internal test sets as well as a public test set.
Year
Venue
Keywords
2010
COLING
source language input,target language word order,target language,public test set,smt system,source side parse tree,improved statistical machine translation,internal test,languages pair,reorder source sentence,word order difference
Field
DocType
Volume
Rule-based machine translation,Example-based machine translation,Word order,Programming language,Parse tree,Computer science,Machine translation,Machine translation software usability,Transfer-based machine translation,Natural language processing,Artificial intelligence,Syntax
Conference
C10-1
Citations 
PageRank 
References 
22
0.71
19
Authors
5
Name
Order
Citations
PageRank
Karthik Visweswariah140038.22
Jiri Navratil231431.36
Jeffrey Sorensen3575.16
V. Chenthamarakshan411412.11
Nanda Kambhatla539051.52