Hierarchical Phrase-Based Translation

  • Authors:
  • David Chiang

  • Affiliations:
  • -

  • Venue:
  • Computational Linguistics
  • Year:
  • 2007

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Abstract

We present a statistical machine translation model that uses hierarchical phrases---phrases that contain subphrases. The model is formally a synchronous context-free grammar but is learned from a parallel text without any syntactic annotations. Thus it can be seen as combining fundamental ideas from both syntax-based translation and phrase-based translation. We describe our system's training and decoding methods in detail, and evaluate it for translation speed and translation accuracy. Using BLEU as a metric of translation accuracy, we find that our system performs significantly better than the Alignment Template System, a state-of-the-art phrase-based system.