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Machine Translation

, Volume 26, Issue 1–2, pp 47–65 | Cite as

A comparison of segmentation methods and extended lexicon models for Arabic statistical machine translation

  • Saša HasanEmail author
  • Saab Mansour
  • Hermann Ney
Article
  • 198 Downloads

Abstract

In this article, we investigate different methodologies of Arabic segmentation for statistical machine translation by comparing a rule-based segmenter to different statistically-based segmenters. We also present a method for segmentation that serves the needs of a real-time translation system without impairing the translation accuracy. Second, we report on extended lexicon models based on triplets that incorporate sentence-level context during the decoding process. Results are presented on different translation tasks that show improvements in both BLEU and TER scores.

Keywords

Statistical machine translation Segmentation Extended lexicon models 

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Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.Human Language Technology and Pattern Recognition Group, Lehrstuhl für Informatik 6RWTH Aachen UniversityAachenGermany

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