Coupling Hierarchical Word Reordering and Decoding in Phrase-Based Statistical Machine Translation

In this paper, we start with the existing idea of taking reordering rules automatically derived from syntactic representations, and applying them in a preprocessing step before translation to make the source sentence structurally more like the target; and we propose a new approach to hierarchically extracting these rules. We evaluate this, combined with a lattice-based decoding, and show improvements over state-of-the-art distortion models.

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