Paper: Getting the Structure Right for Word Alignment: LEAF

ACL ID D07-1006
Title Getting the Structure Right for Word Alignment: LEAF
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2007
Authors

Word alignment is the problem of annotating parallel text with translational correspon- dence. Previous generative word alignment models have made structural assumptions such as the 1-to-1, 1-to-N, or phrase-based consecutive word assumptions, while previ- ous discriminative models have either made such an assumption directly or used features derived from a generative model making one of these assumptions. We present a new gen- erative alignment model which avoids these structural limitations, and show that it is effective when trained using both unsuper- vised and semi-supervised training methods.