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Incorporating Position Information into a Maximum Entropy/Minimum Divergence Translation Model

I describe two methods for incorporating information about the relative positions of bilingual word pairs into a Maximum Entropy/Minimum Divergence translation model. The better of the two achieves over 40\% lower test corpus perplexity than an equivalent combination of a trigram language model and the classical IBM translation model 2.