Incorporating position information into a Maximum Entropy/Minimum Divergence translation model

Authors:
George Foster
Affiliations:
RALI, Université de Montréal
Venue:
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Year:
2000

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Abstract

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.