Abstract
We introduce improvements to statistical word alignment based on the Hidden Markov Model. One improvement incorporates syntactic knowledge. Results on the workshop data show that alignment performance exceeds that of a state-of-the art system based on more complex models, resulting in over a 5.5% absolute reduction in error on Romanian-English.
Cite
CITATION STYLE
Lopez, A., & Resnik, P. (2005). Improved HMM alignment models for languages with scarce resources. In Texts@ACL 2005 - Building and Using Parallel Texts: Data-Driven Machine Translation and Beyond, Proceedings of the Workshop (pp. 83–86). Association for Computational Linguistics (ACL). https://doi.org/10.3115/1654449.1654464
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