Paper: Spectral Clustering For Example Based Machine Translation

ACL ID N06-2011
Title Spectral Clustering For Example Based Machine Translation
Venue Human Language Technologies
Session Short Paper
Year 2006
Authors

Prior work has shown that generaliza- tion of data in an Example Based Ma- chine Translation (EBMT) system, re- duces the amount of pre-translated text re- quired to achieve a certain level of accu- racy (Brown, 2000). Several word clus- tering algorithms have been suggested to perform these generalizations, such as k- Means clustering or Group Average Clus- tering. The hypothesis is that better con- textual clustering can lead to better trans- lation 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% im- provement over the baseline EBMT sys- tem.