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Online Learning via Dynamic Reranking for Computer Assisted Translation

  • Pascual Martínez-Gómez
  • Germán Sanchis-Trilles
  • Francisco Casacuberta
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6609)

Abstract

New techniques for online adaptation in computer assisted translation are explored and compared to previously existing approaches. Under the online adaptation paradigm, the translation system needs to adapt itself to real-world changing scenarios, where training and tuning may only take place once, when the system is set-up for the first time. For this purpose, post-edit information, as described by a given quality measure, is used as valuable feedback within a dynamic reranking algorithm. Two possible approaches are presented and evaluated. The first one relies on the well-known perceptron algorithm, whereas the second one is a novel approach using the Ridge regression in order to compute the optimum scaling factors within a state-of-the-art SMT system. Experimental results show that such algorithms are able to improve translation quality by learning from the errors produced by the system on a sentence-by-sentence basis.

Keywords

Online Learning Machine Translation Ridge Regression Statistical Machine Translation Language Pair 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Pascual Martínez-Gómez
    • 1
  • Germán Sanchis-Trilles
    • 1
  • Francisco Casacuberta
    • 1
  1. 1.Instituto Tecnológico de InformáticaUniversidad Politécnica de ValenciaSpain

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