Estimating Word Translation Probabilities from Unrelated Monolingual Corpora Using the EM Algorithm

Philipp Koehn and Kevin Knight, University of Southern California

Selecting the right word translation among several options in the lexicon is a core problem for machine translation. We present a novel approach to this problem that can be trained using only unrelated monolingual corpora and a lexicon. By estimating word translation probabilities using the EM algorithm, we extend upon target language modeling. We construct a word translation model for 3830 German and 6147 English noun tokens, with very promising results.

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