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Improving statistical MT by coupling reordering and decoding

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Machine Translation

Abstract

In this paper we describe an elegant and efficient approach to coupling reordering and decoding in statistical machine translation, where the n-gram translation model is also employed as distortion model. The reordering search problem is tackled through a set of linguistically motivated rewrite rules, which are used to extend a monotonic search graph with reordering hypotheses. The extended graph is traversed in the global search when a fully informed decision can be taken. Further experiments show that the n-gram translation model can be successfully used as reordering model when estimated with reordered source words. Experiments are reported on the Europarl task (Spanish–English and English–Spanish). Results are presented regarding translation accuracy and computational efficiency, showing significant improvements in translation quality with respect to monotonic search for both translation directions at a very low computational cost.

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  1. Department of Signal Theory and Communications, TALP Research Center, Universitat Politècnica de Catalunya, Campus Norte UPC, Edificio D5, C/Jordi Girona 1-3, 08034, Barcelona, Spain

    Josep Maria Crego & José B. Mariño

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  1. Josep Maria Crego
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  2. José B. Mariño
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Correspondence to Josep Maria Crego.

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Crego, J.M., Mariño, J.B. Improving statistical MT by coupling reordering and decoding. Machine Translation 20, 199–215 (2006). https://doi.org/10.1007/s10590-007-9024-z

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