Spectral clustering for example based machine translation

Authors:
Rashmi Gangadharaiah;Ralf Brown;Jaime Carbonell
Affiliations:
Carnegie Mellon University, Pittsburgh, P.A.;Carnegie Mellon University, Pittsburgh, P.A.;Carnegie Mellon University, Pittsburgh, P.A.
Venue:
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
Year:
2006

Citing 3
Cited 4

A statistical approach to machine translation

Computational Linguistics
Automated generalization of translation examples

COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
BLEU: a method for automatic evaluation of machine translation

ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics

Spectral clustering for Chinese word

FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
Phrasal equivalence classes for generalized corpus-based machine translation

CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part II
Panning for EBMT gold, or "Remembering not to forget"

Machine Translation
The CMU-EBMT machine translation system

Machine Translation

Quantified Score

Hi-index	0.00

Visualization

Abstract

Prior work has shown that generalization of data in an Example Based Machine Translation (EBMT) system, reduces the amount of pre-translated text required to achieve a certain level of accuracy (Brown, 2000). Several word clustering algorithms have been suggested to perform these generalizations, such as k-Means clustering or Group Average Clustering. The hypothesis is that better contextual clustering can lead to better translation accuracy with limited training data. In this paper, we use a form of spectral clustering to cluster words, and this is shown to result in as much as 29.08% improvement over the baseline EBMT system.