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Bilingual Data Selection Using a Continuous Vector-Space Representation

  • Mara Chinea-RiosEmail author
  • Germán Sanchis-Trilles
  • Francisco Casacuberta
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10029)

Abstract

Data selection aims to select the best data subset from an available pool of sentences with which to train a pattern recognition system. In this article, we present a bilingual data selection method that leverages a continuous vector-space representation of word sequences for selecting the best subset of a bilingual corpus, for the application of training a machine translation system. We compared our proposal with a state-of-the-art data selection technique (cross-entropy) obtaining very promising results, which were coherent across different language pairs.

Keywords

Vector space representation Data selection Bilingual corpora 

Notes

Acknowledgments

The research leading to these results has received funding from the Generalitat Valenciana under grant PROMETEOII/2014/030 and the FPI (2014) grant by Universitat Politècnica de València.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Mara Chinea-Rios
    • 1
    Email author
  • Germán Sanchis-Trilles
    • 2
  • Francisco Casacuberta
    • 1
  1. 1.Pattern Recognition and Human Language Technology Research CenterUniversitat Politècnica de ValènciaValenciaSpain
  2. 2.ScilingUniversitat Politècnica de ValènciaValenciaSpain

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