Paper: Improving Statistical Word Alignment with Ensemble Methods

ACL ID I05-1041
Title Improving Statistical Word Alignment with Ensemble Methods
Venue International Joint Conference on Natural Language Processing
Session Main Conference
Year 2005
Authors

This paper proposes an approach to improve statistical word align- ment with ensemble methods. Two ensemble methods are investigated: bagging and cross-validation committees. On these two methods, both weighted voting and unweighted voting are compared under the word alignment task. In addi- tion, we analyze the effect of different sizes of training sets on the bagging method. Experimental results indicate that both bagging and cross-validation committees improve the word alignment results regardless of weighted voting or unweighted voting. Weighted voting performs consistently better than un- weighted voting on different sizes of training sets.