N-gram posterior probabilities for statistical machine translation

  • Authors:
  • Richard Zens;Hermann Ney

  • Affiliations:
  • RWTH Aachen University, Aachen, Germany;RWTH Aachen University, Aachen, Germany

  • Venue:
  • StatMT '06 Proceedings of the Workshop on Statistical Machine Translation
  • Year:
  • 2006

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Abstract

Word posterior probabilities are a common approach for confidence estimation in automatic speech recognition and machine translation. We will generalize this idea and introduce n-gram posterior probabilities and show how these can be used to improve translation quality. Additionally, we will introduce a sentence length model based on posterior probabilities. We will show significant improvements on the Chinese-English NIST task. The absolute improvements of the BLEU score is between 1.1% and 1.6%.