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.
This model shows a significant improvement over the state-of-the-art hierarchical phrase-based system REF .
A Hierarchical Phrase-Based Model For Statistical Machine Translation
Annual Meeting Of The Association For Computational Linguistics
Computer Science