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

  • Authors:
  • Saša Hasan;Saab Mansour;Hermann Ney

  • Affiliations:
  • Human Language Technology and Pattern Recognition Group, Lehrstuhl für Informatik 6, RWTH Aachen University, Aachen, Germany 52062;Human Language Technology and Pattern Recognition Group, Lehrstuhl für Informatik 6, RWTH Aachen University, Aachen, Germany 52062;Human Language Technology and Pattern Recognition Group, Lehrstuhl für Informatik 6, RWTH Aachen University, Aachen, Germany 52062

  • Venue:
  • Machine Translation
  • Year:
  • 2012

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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.