A hierarchical phrase-based model for statistical machine translation

We present a statistical phrase-based translation model that uses hierarchical phrases---phrases that contain subphrases. The model is formally a synchronous context-free grammar but is learned from a bitext without any syntactic information. Thus it can be seen as a shift to the formal machinery of syntax-based translation systems without any linguistic commitment. In our experiments using BLEU as a metric, the hierarchical phrase-based model achieves a relative improvement of 7.5% over Pharaoh, a state-of-the-art phrase-based system.

• UD
  U.S. Department of Defense
• MU
  Multidisciplinary University Research Initiative
• OO
  Office of Naval Research

  Award: FCPO.810548265