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• W1736600331 abstract "A principal weakness of conventional (i.e., non-hierarchical) phrase-based statistical machine translation is that it can only exploit continuous phrases. In this paper, we extend phrase-based decoding to allow both source and target phrasal discontinuities, which provide better generalization on unseen data and yield significant improvements to a standard phrase-based system (Moses). More interestingly, our discontinuous phrase-based system also outperforms a state-of-the-art hierarchical system (Joshua) by a very significant margin (+1.03 BLEU on average on five Chinese-English NIST test sets), even though both Joshua and our system support discontinuous phrases. Since the key difference between these two systems is that ours is not hierarchical---i.e., our system uses a string-based decoder instead of CKY, and it imposes no hard hierarchical reordering constraints during training and decoding---this paper sets out to challenge the commonly held belief that the tree-based parameterization of systems such as Hiero and Joshua is crucial to their good performance against Moses." @default.
• W1736600331 created "2016-06-24" @default.
• W1736600331 creator A5037066465 @default.
• W1736600331 creator A5046006076 @default.
• W1736600331 date "2010-06-02" @default.
• W1736600331 modified "2023-09-25" @default.
• W1736600331 title "Accurate Non-Hierarchical Phrase-Based Translation" @default.
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