{"id":151885,"date":"2005-06-01T00:00:00","date_gmt":"2005-06-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/dependency-treelet-translation-syntactically-informed-phrasal-smt\/"},"modified":"2018-10-16T21:59:35","modified_gmt":"2018-10-17T04:59:35","slug":"dependency-treelet-translation-syntactically-informed-phrasal-smt","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/dependency-treelet-translation-syntactically-informed-phrasal-smt\/","title":{"rendered":"Dependency Treelet Translation: Syntactically Informed Phrasal SMT"},"content":{"rendered":"<div class=\"asset-content\">\n<p>We describe a novel approach to statistical machine translation that combines syntactic information in the source language with recent advances in phrasal translation. This method requires a source-language dependency parser, target language word segmentation and an unsupervised word alignment component. We align a parallel corpus, project the source dependency parse onto the target sentence, extract dependency treelet translation pairs, and train a tree-based ordering model. We describe an efficient decoder and show that using these treebased models in combination with conventional SMT models provides a promising approach that incorporates the power of phrasal SMT with the linguistic generality available in a parser.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We describe a novel approach to statistical machine translation that combines syntactic information in the source language with recent advances in phrasal translation. This method requires a source-language dependency parser, target language word segmentation and an unsupervised word alignment component. 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