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Machine Translation

, Volume 28, Issue 3–4, pp 309–339 | Cite as

Online adaptation to post-edits for phrase-based statistical machine translation

  • Nicola Bertoldi
  • Patrick Simianer
  • Mauro Cettolo
  • Katharina Wäschle
  • Marcello Federico
  • Stefan Riezler
Article
First Online:
Received:
Accepted:

Abstract

Recent research has shown that accuracy and speed of human translators can benefit from post-editing output of machine translation systems, with larger benefits for higher quality output. We present an efficient online learning framework for adapting all modules of a phrase-based statistical machine translation system to post-edited translations. We use a constrained search technique to extract new phrase-translations from post-edits without the need of re-alignments, and to extract phrase pair features for discriminative training without the need for surrogate references. In addition, a cache-based language model is built on \(n\)-grams extracted from post-edits. We present experimental results in a simulated post-editing scenario and on field-test data. Each individual module substantially improves translation quality. The modules can be implemented efficiently and allow for a straightforward stacking, yielding significant additive improvements on several translation directions and domains.

Keywords

Statistical machine translation Post-editing Online adaptation 
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Notes

Acknowledgments

FBK researchers were supported by the MateCat project, funded by the EC under FP7; researchers at Heidelberg University by DFG grant “Cross-language Learning-to-Rank for Patent Retrieval”.

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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Nicola Bertoldi
    • 1
  • Patrick Simianer
    • 2
  • Mauro Cettolo
    • 1
  • Katharina Wäschle
    • 2
  • Marcello Federico
    • 1
  • Stefan Riezler
    • 2
  1. 1.FBK - Fondazione Bruno KesslerTrentoItaly
  2. 2.Department of Computational LinguisticsHeidelberg UniversityHeidelbergGermany

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