| other,14-3-P05-1034,bq | dependency parse </term> onto the target <term> | sentence | </term> , extract <term> dependency treelet | #9258 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. |
| tech,36-4-P05-1034,bq | linguistic generality available in a <term> | parser | </term> . We directly investigate a subject | #9309 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 a parser . |
| tech,4-4-P05-1034,bq | </term> . We describe an efficient <term> | decoder | </term> and show that using these <term> tree-based | #9277 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 a parser. |
| tech,1-2-P05-1034,bq | <term> phrasal translation </term> . This <term> | method | </term> requires a <term> source-language </term> | #9226 This method requires a source-language dependency parser, target language word segmentation and an unsupervised word alignment component. |
| tech,4-1-P05-1034,bq | quality </term> . We describe a novel <term> | approach | </term> to <term> statistical machine translation | #9205 We describe a novel approach to statistical machine translation that combines syntactic information in the source language with recent advances in phrasal translation. |
