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Generating Complex Morphology for Machine Translation

Einat Minkov, Kristina Toutanova, and Hisami Suzuki

Abstract

We present a novel method for predicting inflected word forms for generating morphologically rich languages in machine translation. We utilize a rich set of syntactic and morphological knowledge sources from both source and target sentences in a probabilistic model, and evaluate their contribution in generating Russian and Arabic sentences. Our results show that the proposed model substantially outperforms the commonly used baseline of a trigram target language model; in particular, the use of morphological and syntactic features leads to large gains in prediction accuracy. We also show that the proposed method is effective with a relatively small amount of data.

Details

Publication typeInproceedings
Published inProceedings of ACL
URLhttp://aclweb.org/anthology-new/P/P07/P07-1017.pdf
PublisherAssociation for Computational Linguistics
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