This paper proposes a novel semi-supervised word alignment technique called EMDC that integrates discriminative and generative methods. A discriminative aligner is used to find high precision partial alignments that serve as constraints for a generative aligner which implements a constrained version of the EM algorithm. Experiments on small-size Chinese and Arabic tasks show consistent improvements on AER. We also experimented with moderate-size Chinese machine translation tasks and got an average of 0.5 point improvement on BLEU scores across five standard NIST test sets and four other test sets.

@inproceedings{gao:2010:emdc, Author = {Gao, Qin and Guzm{\'a}n, Francisco and Vogel, Stephan}, Booktitle = {Proceedings of the 23rd International Conference on Computational Linguistics {(COLING 2010)}}, Organization = {Association for Computational Linguistics}, Pages = {349--357}, Title = {EMDC: a semi-supervised approach for word alignment}, address = {Beijing, China}, Year = {2010}, Abstract= This paper proposes a novel semi-supervised word alignment technique called EMDC that integrates discriminative and generative methods. A discriminative aligner is used to find high precision partial alignments that serve as constraints for a generative aligner which implements a constrained version of the EM algorithm. Experiments on small-size Chinese and Arabic tasks show consistent improvements on AER. We also experimented with moderate-size Chinese machine translation tasks and got an average of 0.5 point improvement on BLEU scores across five standard NIST test sets and four other test sets.}