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 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. 1

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 phrasebased system. 1.

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This paper presents an algorithm for generating and filtering an invertible and structural analogous translation grammar from bilingual aligned and linguistically bracketed texts. The algorithm is discussed in general terms and applied to German-English alignments.

This paper presents an algorithm for extracting invertible probabilistic translation grammars from bilingual aligned and linguistically bracketed text. The invertibility condition requires all translation ambiguities to be resolved in the final translation grammar. The paper examines the complexity of inducing translation grammars and proposes a number of heuristics to reduce the the theoretically exponential computation time.

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