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Improving the Minimum Description Length Inference of Phrase-Based Translation Models

  • Jesús González-RubioEmail author
  • Francisco Casacuberta
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9117)

Abstract

We study the application of minimum description length (MDL) inference to estimate pattern recognition models for machine translation. MDL is a theoretically-sound approach whose empirical results are however below those of the state-of-the-art pipeline of training heuristics. We identify potential limitations of current MDL procedures and provide a practical approach to overcome them. Empirical results support the soundness of the proposed approach.

Notes

Acknowledgments

Work supported by the EU \(7^\mathrm{th}\) Framework Programme (FP7/2007–2013) under the CasMaCat project (grant agreement n\(^{\text{ o }}\) 287576), by Spanish MICINN under grant TIN2012-31723, and by the Generalitat Valenciana under grant ALMPR (Prometeo/2009/014).

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Pattern Recognition and Human Language Technology CenterUniversitat Politècnica de ValènciaValenciaSpain

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