| tech,6-1-P05-1034,ak | </term> . We describe a novel approach to <term> | statistical machine translation | </term> that combines <term> syntactic information | #8845 We describe a novel approach tostatistical machine translation that combines syntactic information in the source language with recent advances in phrasal translation. | |
| other,11-1-P05-1034,ak | machine translation </term> that combines <term> | syntactic information | </term> in the <term> source language </term> | #8850 We describe a novel approach to statistical machine translation that combinessyntactic information in the source language with recent advances in phrasal translation. | |
| other,15-1-P05-1034,ak | syntactic information </term> in the <term> | source language | </term> with recent advances in <term> phrasal | #8854 We describe a novel approach to statistical machine translation that combines syntactic information in thesource language with recent advances in phrasal translation. | |
| tech,21-1-P05-1034,ak | language </term> with recent advances in <term> | phrasal translation | </term> . This method requires a <term> source-language | #8860 We describe a novel approach to statistical machine translation that combines syntactic information in the source language with recent advances inphrasal translation. | |
| other,4-2-P05-1034,ak | translation </term> . This method requires a <term> | source-language | </term><term> dependency parser </term> , <term> | #8867 This method requires asource-language dependency parser, target language word segmentation and an unsupervised word alignment component. | |
| tech,5-2-P05-1034,ak | requires a <term> source-language </term><term> | dependency parser | </term> , <term> target language </term><term> | #8868 This method requires a source-languagedependency parser, target language word segmentation and an unsupervised word alignment component. | |
| other,8-2-P05-1034,ak | </term><term> dependency parser </term> , <term> | target language | </term><term> word segmentation </term> and | #8871 This method requires a source-language dependency parser,target language word segmentation and an unsupervised word alignment component. | |
| tech,10-2-P05-1034,ak | </term> , <term> target language </term><term> | word segmentation | </term> and an <term> unsupervised word alignment | #8873 This method requires a source-language dependency parser, target languageword segmentation and an unsupervised word alignment component. | |
| other,14-2-P05-1034,ak | <term> word segmentation </term> and an <term> | unsupervised word alignment component | </term> . We align a <term> parallel corpus | #8877 This method requires a source-language dependency parser, target language word segmentation and anunsupervised word alignment component. | |
| lr,3-3-P05-1034,ak | alignment component </term> . We align a <term> | parallel corpus | </term> , project the <term> source dependency | #8885 We align aparallel corpus, project the source dependency parse onto the target sentence, extract dependency treelet translation pairs, and train a tree-based ordering model. | |
| other,8-3-P05-1034,ak | parallel corpus </term> , project the <term> | source dependency parse | </term> onto the <term> target sentence </term> | #8890 We align a parallel corpus, project thesource dependency parse onto the target sentence, extract dependency treelet translation pairs, and train a tree-based ordering model. | |
| other,13-3-P05-1034,ak | source dependency parse </term> onto the <term> | target sentence | </term> , extract <term> dependency treelet | #8895 We align a parallel corpus, project the source dependency parse onto thetarget sentence, extract dependency treelet translation pairs, and train a tree-based ordering model. | |
| other,17-3-P05-1034,ak | <term> target sentence </term> , extract <term> | dependency treelet translation pairs | </term> , and train a <term> tree-based ordering | #8899 We align a parallel corpus, project the source dependency parse onto the target sentence, extractdependency treelet translation pairs, and train a tree-based ordering model. | |
| model,25-3-P05-1034,ak | translation pairs </term> , and train a <term> | tree-based ordering model | </term> . We describe an efficient <term> decoder | #8907 We align a parallel corpus, project the source dependency parse onto the target sentence, extract dependency treelet translation pairs, and train atree-based ordering model. | |
| tech,4-4-P05-1034,ak | model </term> . We describe an efficient <term> | decoder | </term> and show that using these <term> tree-based | #8915 We describe an efficientdecoder and show that using these tree-based 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. | |
| model,10-4-P05-1034,ak | decoder </term> and show that using these <term> | tree-based models | </term> in combination with conventional <term> | #8921 We describe an efficient decoder and show that using thesetree-based 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. | |
| model,16-4-P05-1034,ak | </term> in combination with conventional <term> | SMT models | </term> provides a promising approach that | #8927 We describe an efficient decoder and show that using these tree-based models in combination with conventionalSMT models provides a promising approach that incorporates the power of phrasal SMT with the linguistic generality available in a parser. | |
| tech,27-4-P05-1034,ak | approach that incorporates the power of <term> | phrasal SMT | </term> with the linguistic generality available | #8938 We describe an efficient decoder and show that using these tree-based models in combination with conventional SMT models provides a promising approach that incorporates the power ofphrasal SMT with the linguistic generality available in a parser. | |
| tech,36-4-P05-1034,ak | linguistic generality available in a <term> | parser | </term> . In this paper , we present an <term> | #8947 We describe an efficient decoder and show that using these tree-based models in combination with conventional SMT models provides a promising approach that incorporates the power of phrasal SMT with the linguistic generality available in aparser. |
