| tech,0-1-P05-1067,ak | the state-of-the-art technologies . <term> | Syntax-based statistical machine translation ( MT ) | </term> aims at applying <term> statistical | #9788 Experimental results show that our approach improves domain-specific word alignment in terms of both precision and recall, achieving a relative error rate reduction of 6.56% as compared with the state-of-the-art technologies.Syntax-based statistical machine translation ( MT ) aims at applying statistical models to structured data. | |
| model,10-1-P05-1067,ak | translation ( MT ) </term> aims at applying <term> | statistical models | </term> to <term> structured data </term> . In | #9798 Syntax-based statistical machine translation (MT) aims at applyingstatistical models to structured data. | |
| other,13-1-P05-1067,ak | applying <term> statistical models </term> to <term> | structured data | </term> . In this paper , we present a <term> | #9801 Syntax-based statistical machine translation (MT) aims at applying statistical models tostructured data. | |
| tech,7-2-P05-1067,ak | </term> . In this paper , we present a <term> | syntax-based statistical machine translation system | </term> based on a <term> probabilistic synchronous | #9811 In this paper, we present asyntax-based statistical machine translation system based on a probabilistic synchronous dependency insertion grammar. | |
| lr,15-2-P05-1067,ak | translation system </term> based on a <term> | probabilistic synchronous dependency insertion grammar | </term> . <term> Synchronous dependency insertion | #9819 In this paper, we present a syntax-based statistical machine translation system based on aprobabilistic synchronous dependency insertion grammar. | |
| lr,0-3-P05-1067,ak | dependency insertion grammar </term> . <term> | Synchronous dependency insertion grammars | </term> are a version of <term> synchronous | #9825 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. | |
| lr,8-3-P05-1067,ak | insertion grammars </term> are a version of <term> | synchronous grammars | </term> defined on <term> dependency trees </term> | #9833 Synchronous dependency insertion grammars are a version ofsynchronous grammars defined on dependency trees. | |
| lr,12-3-P05-1067,ak | synchronous grammars </term> defined on <term> | dependency trees | </term> . We first introduce our approach | #9837 Synchronous dependency insertion grammars are a version of synchronous grammars defined ondependency trees. | |
| lr,9-4-P05-1067,ak | introduce our approach to inducing such a <term> | grammar | </term> from <term> parallel corpora </term> | #9849 We first introduce our approach to inducing such agrammar from parallel corpora. | |
| lr,11-4-P05-1067,ak | inducing such a <term> grammar </term> from <term> | parallel corpora | </term> . Second , we describe the <term> graphical | #9851 We first introduce our approach to inducing such a grammar fromparallel corpora. | |
| model,5-5-P05-1067,ak | corpora </term> . Second , we describe the <term> | graphical model | </term> for the <term> machine translation | #9859 Second, we describe thegraphical model for the machine translation task, which can also be viewed as a stochastic tree-to-tree transducer. | |
| other,9-5-P05-1067,ak | <term> graphical model </term> for the <term> | machine translation task | </term> , which can also be viewed as a <term> | #9863 Second, we describe the graphical model for themachine translation task, which can also be viewed as a stochastic tree-to-tree transducer. | |
| tech,20-5-P05-1067,ak | </term> , which can also be viewed as a <term> | stochastic tree-to-tree transducer | </term> . We introduce a <term> polynomial | #9874 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,ak | transducer </term> . We introduce a <term> | polynomial time decoding algorithm | </term> for the <term> model </term> . We evaluate | #9881 We introduce apolynomial time decoding algorithm for the model. | |
| model,9-6-P05-1067,ak | time decoding algorithm </term> for the <term> | model | </term> . We evaluate the outputs of our <term> | #9887 We introduce a polynomial time decoding algorithm for themodel. | |
| tech,6-7-P05-1067,ak | </term> . We evaluate the outputs of our <term> | MT system | </term> using the <term> NIST </term> and <term> | #9895 We evaluate the outputs of ourMT system using the NIST and Bleu automatic MT evaluation software. | |
| measure(ment),10-7-P05-1067,ak | our <term> MT system </term> using the <term> | NIST | </term> and <term> Bleu automatic MT evaluation | #9899 We evaluate the outputs of our MT system using theNIST and Bleu automatic MT evaluation software. | |
| measure(ment),12-7-P05-1067,ak | </term> using the <term> NIST </term> and <term> | Bleu automatic MT evaluation software | </term> . The result shows that our system | #9901 We evaluate the outputs of our MT system using the NIST andBleu automatic MT evaluation software. | |
| tech,8-8-P05-1067,ak | shows that our system outperforms the <term> | baseline system | </term> based on the <term> IBM models </term> | #9915 The result shows that our system outperforms thebaseline system based on the IBM models in both translation speed and quality. | |
| model,13-8-P05-1067,ak | baseline system </term> based on the <term> | IBM models | </term> in both <term> translation speed and | #9920 The result shows that our system outperforms the baseline system based on theIBM models in both translation speed and quality. |
