Báo cáo khoa học: "Learning to Transform and Select Elementary Trees for Improved Syntax-based Machine Translations"
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We propose a novel technique of learning how to transform the source parse trees to improve the translation qualities of syntax-based translation models using synchronous context-free grammars. We transform the source tree phrasal structure into a set of simpler structures, expose such decisions to the decoding process, and find the least expensive transformation operation to better model word reordering.
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