Paper: Improving Word Alignment Quality Using Morpho-Syntactic Information

ACL ID C04-1045
Title Improving Word Alignment Quality Using Morpho-Syntactic Information
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2004
Authors

In this paper, we present an approach to include morpho-syntactic dependencies into the training of the statistical alignment models. Existing statistical translation sys- tems usually treat different derivations of the same base form as they were indepen- dent of each other. We propose a method which explicitly takes into account such in- terdependencies during the EM training of the statistical alignment models. The eval- uation is done by comparing the obtained Viterbi alignments with a manually anno- tated reference alignment. The improve- ments of the alignment quality compared to the, to our knowledge, best system are reported on the German-English Verbmobil corpus.