Hierarchical Phrase-Based Translation

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.

• UD
  U.S. Department of Defense
• DA
  Defense Advanced Research Projects Agency
• AR
  Advanced Research Projects Agency
• MU
  Multidisciplinary University Research Initiative
• OO
  Office of Naval Research

  Award: FCPO.810548265