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» Better Word Alignments with Supervised ITG Models
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ACL
2009
13 years 2 months ago
Better Word Alignments with Supervised ITG Models
This work investigates supervised word alignment methods that exploit inversion transduction grammar (ITG) constraints. We consider maximum margin and conditional likelihood objec...
Aria Haghighi, John Blitzer, John DeNero, Dan Klei...
COLING
2010
12 years 11 months ago
Improved Discriminative ITG Alignment using Hierarchical Phrase Pairs and Semi-supervised Training
While ITG has many desirable properties for word alignment, it still suffers from the limitation of one-to-one matching. While existing approaches relax this limitation using phra...
Shujie Liu, Chi-Ho Li, Ming Zhou
ACL
2006
13 years 6 months ago
Soft Syntactic Constraints for Word Alignment through Discriminative Training
Word alignment methods can gain valuable guidance by ensuring that their alignments maintain cohesion with respect to the phrases specified by a monolingual dependency tree. Howev...
Colin Cherry, Dekang Lin
NAACL
2010
13 years 2 months ago
Unsupervised Syntactic Alignment with Inversion Transduction Grammars
Syntactic machine translation systems currently use word alignments to infer syntactic correspondences between the source and target languages. Instead, we propose an unsupervised...
Adam Pauls, Dan Klein, David Chiang, Kevin Knight
EMNLP
2010
13 years 2 months ago
Combining Unsupervised and Supervised Alignments for MT: An Empirical Study
Word alignment plays a central role in statistical MT (SMT) since almost all SMT systems extract translation rules from word aligned parallel training data. While most SMT systems...
Jinxi Xu, Antti-Veikko I. Rosti