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.

• Publication year

  2005

• Venue

  Annual Meeting of the Association for Computational Linguistics

• Publication date

  2005-06-25

• Fields of study

  Linguistics, Computer Science

• Identifiers
  DOI 10.3115/1219840.1219873
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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