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Titlebook: Machine Translation: From Research to Real Users; 5th Conference of th Stephen D. Richardson Conference proceedings 2002 Springer-Verlag Be

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DUSTer: A Method for Unraveling Cross-Language Divergences for Statistical Word-Level Alignmentwe introduce DUSTer, a method for systematically identifying common divergence types and transforming an English sentence structure to bear a closer resemblance to that of another language. Our ultimate goal is to enable more accurate alignment and projection of dependency trees in another language
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Text Prediction with Fuzzy Alignmentsstatistical translation models can be applied within a usermodeling framework to improve translator productivity by over 10% in simulated results. For the sake of efficiency in making real-time predictions, these models ignore the alignment relation between source and target texts. In this paper we
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Example-Based Machine Translation via the Webose an example-based machine translation system which uses a ‘phrasal lexicon’ in addition to the aligned sentences in its database. These phrases are extracted from the Penn Treebank using the Marker Hypothesis as a constraint on segmentation. They are then translated by three on-line machine trans
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