We present a statistical phrase-based translation model that uses hierarchical phrasesphrases 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 syntaxbased translation systems without any linguistic commitment. In our experiments using BLEU as a metric, the hierarchical phrasebased model achieves a relative improvement of 7.5% over Pharaoh, a state-of-the-art phrase-based system.
In hierarchical SMT REF , a Synchronous Context Free Grammar (SCFG) is learned from a parallel corpus.
A Hierarchical Phrase-Based Model For Statistical Machine Translation
Annual Meeting Of The Association For Computational Linguistics
Computer Science