| lr,11-4-P05-1067,bq | inducing such a <term> grammar </term> from <term> | parallel corpora | </term> . Second , we describe the <term> graphical | #9471 We first introduce our approach to inducing such a grammar fromparallel corpora. |
| measure(ment),10-7-P05-1067,bq | our <term> MT system </term> using the <term> | NIST and Bleu automatic MT evaluation software | </term> . The result shows that our <term> | #9519 We evaluate the outputs of our MT system using theNIST and Bleu automatic MT evaluation software. |
| measure(ment),17-8-P05-1067,bq | the <term> IBM models </term> in both <term> | translation speed and quality | </term> . In this paper , we present a novel | #9544 The result shows that our system outperforms the baseline system based on the IBM models in bothtranslation speed and quality. |
| other,0-3-P05-1067,bq | dependency insertion grammar </term> . <term> | Synchronous dependency insertion grammars | </term> are a version of <term> synchronous | #9445 In this paper, we present a syntax-based statistical machine translation system based on a probabilistic synchronous dependency insertion grammar.Synchronous dependency insertion grammars are a version of synchronous grammars defined on dependency trees. |
| other,12-3-P05-1067,bq | synchronous grammars </term> defined on <term> | dependency trees | </term> . We first introduce our <term> approach | #9457 Synchronous dependency insertion grammars are a version of synchronous grammars defined ondependency trees. |
| other,13-1-P05-1067,bq | applying <term> statistical models </term> to <term> | structured data | </term> . In this paper , we present a <term> | #9421 Syntax-based statistical machine translation (MT) aims at applying statistical models tostructured data. |
| other,15-2-P05-1067,bq | translation system </term> based on a <term> | probabilistic synchronous dependency insertion grammar | </term> . <term> Synchronous dependency insertion | #9439 In this paper, we present a syntax-based statistical machine translation system based on aprobabilistic synchronous dependency insertion grammar. |
| other,8-3-P05-1067,bq | insertion grammars </term> are a version of <term> | synchronous grammars | </term> defined on <term> dependency trees </term> | #9453 Synchronous dependency insertion grammars are a version ofsynchronous grammars defined on dependency trees. |
| other,8-8-P05-1067,bq | <term> system </term> outperforms the <term> | baseline system | </term> based on the <term> IBM models </term> | #9535 The result shows that our system outperforms thebaseline system based on the IBM models in both translation speed and quality. |
| other,9-4-P05-1067,bq | <term> approach </term> to inducing such a <term> | grammar | </term> from <term> parallel corpora </term> | #9469 We first introduce our approach to inducing such agrammar from parallel corpora. |
| other,9-5-P05-1067,bq | <term> graphical model </term> for the <term> | machine translation task | </term> , which can also be viewed as a <term> | #9483 Second, we describe the graphical model for themachine translation task, which can also be viewed as a stochastic tree-to-tree transducer. |
| tech,0-1-P05-1067,bq | statistical MT architectures </term> . <term> | Syntax-based statistical machine translation ( MT ) | </term> aims at applying <term> statistical | #9408 Error analysis suggests several key factors behind this surprising finding, including inherent limitations of current statistical MT architectures.Syntax-based statistical machine translation ( MT ) aims at applying statistical models to structured data. |
| tech,10-1-P05-1067,bq | translation ( MT ) </term> aims at applying <term> | statistical models | </term> to <term> structured data </term> . In | #9418 Syntax-based statistical machine translation (MT) aims at applyingstatistical models to structured data. |
| tech,13-8-P05-1067,bq | baseline system </term> based on the <term> | IBM models | </term> in both <term> translation speed and | #9540 The result shows that our system outperforms the baseline system based on theIBM models in both translation speed and quality. |
| tech,20-5-P05-1067,bq | </term> , which can also be viewed as a <term> | stochastic tree-to-tree transducer | </term> . We introduce a <term> polynomial | #9494 Second, we describe the graphical model for the machine translation task, which can also be viewed as astochastic tree-to-tree transducer. |
| tech,3-6-P05-1067,bq | transducer </term> . We introduce a <term> | polynomial time decoding algorithm | </term> for the <term> model </term> . We evaluate | #9501 We introduce apolynomial time decoding algorithm for the model. |
| tech,3-7-P05-1067,bq | <term> model </term> . We evaluate the <term> | outputs | </term> of our <term> MT system </term> using | #9512 We evaluate theoutputs of our MT system using the NIST and Bleu automatic MT evaluation software. |
| tech,4-4-P05-1067,bq | trees </term> . We first introduce our <term> | approach | </term> to inducing such a <term> grammar </term> | #9464 We first introduce ourapproach to inducing such a grammar from parallel corpora. |
| tech,5-5-P05-1067,bq | corpora </term> . Second , we describe the <term> | graphical model | </term> for the <term> machine translation | #9479 Second, we describe thegraphical model for the machine translation task, which can also be viewed as a stochastic tree-to-tree transducer. |
| tech,5-8-P05-1067,bq | </term> . The result shows that our <term> | system | </term> outperforms the <term> baseline system | #9532 The result shows that oursystem outperforms the baseline system based on the IBM models in both translation speed and quality. |
