Paper: Improved Discriminative ITG Alignment using Hierarchical Phrase Pairs and Semi-supervised Training

ACL ID C10-2084
Title Improved Discriminative ITG Alignment using Hierarchical Phrase Pairs and Semi-supervised Training
Venue International Conference on Computational Linguistics
Session Poster Session
Year 2010
Authors

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 li- mitation using phrase pairs, we propose a ITG formalism, which even handles units of non-contiguous words, using both simple and hierarchical phrase pairs. We also propose a parameter estimation me- thod, which combines the merits of both supervised and unsupervised learning, for the ITG formalism. The ITG align- ment system achieves significant im- provement in both word alignment quali- ty and translation performance.