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 stateof-the-art distortion models.
CitationDras, M.; Khalilov, M.; Fonollosa, José A. R. Coupling hierarchical word reordering and decoding in phrase-based statistical machine translation. A: Association for Computational Linguistics. North American Chapter. Conference. "North American Chapter of the Association for Computational Linguistics - Human Language Technologies (NAACL HLT) 2009 conference". Boulder, Colorado: 2009, p. 78-86.
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