HMM-based word alignment in statistical translation
作者： Stephan Vogel , Hermann Ney , Christoph Tillmann
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摘要: In this paper, we describe a new model for word alignment in statistical translation and present experimental results. The idea of the is to make probabilities dependent on differences positions rather than absolute positions. To achieve goal, approach uses first-order Hidden Markov (HMM) problem as they are used successfully speech recognition time problem. difference HMM that there no monotony constraint possible orderings. We details test several bilingual corpora.
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