| tech,7-3-A94-1017,ak | We have already proposed a model , <term> TDMT ( Transfer-Driven Machine Translation ) </term> , that translates a <term> sentence </term> utilizing <term> examples </term> effectively and performs accurate <term> structural disambiguation </term> and <term> target word selection </term> . | #26120 We have already proposed a model,TDMT ( Transfer-Driven Machine Translation ), that translates a sentence utilizing examples effectively and performs accurate structural disambiguation and target word selection. | |
| tech,5-9-A94-1017,ak | Thus , our model , <term> TDMT </term> on <term> APs </term> , meets the vital requirements of <term> spoken language translation </term> . | #26238 Thus, our model,TDMT on APs, meets the vital requirements of spoken language translation. | |
| tech,3-5-A94-1017,ak | In <term> TDMT </term> , <term> example-retrieval ( ER ) </term> , i.e. , retrieving <term> examples </term> most similar to an <term> input expression </term> , is the most dominant part of the total processing time . | #26156 In TDMT,example-retrieval ( ER ), i.e., retrieving examples most similar to an input expression, is the most dominant part of the total processing time. | |
| tech,0-2-A94-1017,ak | This paper proposes a model using <term> associative processors ( APs ) </term> for <term> real-time spoken language translation </term> . <term> Spoken language translation </term> requires ( 1 ) an accurate <term> translation </term> and ( 2 ) a real-time response . | #26095 This paper proposes a model using associative processors (APs) for real-time spoken language translation.Spoken language translation requires (1) an accurate translation and (2) a real-time response. | |
| other,16-6-A94-1017,ak | Our study has concluded that we only need to implement the <term> ER </term> for <term> expressions </term> including a <term> frequent word </term> on <term> APs </term> . | #26199 Our study has concluded that we only need to implement the ER for expressions including afrequent word on APs. | |
| other,17-3-A94-1017,ak | We have already proposed a model , <term> TDMT ( Transfer-Driven Machine Translation ) </term> , that translates a <term> sentence </term> utilizing <term> examples </term> effectively and performs accurate <term> structural disambiguation </term> and <term> target word selection </term> . | #26130 We have already proposed a model, TDMT (Transfer-Driven Machine Translation), that translates asentence utilizing examples effectively and performs accurate structural disambiguation and target word selection. | |
| tech,9-2-A94-1017,ak | <term> Spoken language translation </term> requires ( 1 ) an accurate <term> translation </term> and ( 2 ) a real-time response . | #26104 Spoken language translation requires (1) an accuratetranslation and (2) a real-time response. | |
| tech,24-3-A94-1017,ak | We have already proposed a model , <term> TDMT ( Transfer-Driven Machine Translation ) </term> , that translates a <term> sentence </term> utilizing <term> examples </term> effectively and performs accurate <term> structural disambiguation </term> and <term> target word selection </term> . | #26137 We have already proposed a model, TDMT (Transfer-Driven Machine Translation), that translates a sentence utilizing examples effectively and performs accuratestructural disambiguation and target word selection. | |
| other,12-8-A94-1017,ak | Moreover , a study on communications between <term> APs </term> demonstrates the <term> scalability </term> against <term> vocabulary size </term> by <term> extrapolation </term> . | #26228 Moreover, a study on communications between APs demonstrates the scalability againstvocabulary size by extrapolation. | |
| other,16-5-A94-1017,ak | In <term> TDMT </term> , <term> example-retrieval ( ER ) </term> , i.e. , retrieving <term> examples </term> most similar to an <term> input expression </term> , is the most dominant part of the total processing time . | #26169 In TDMT, example-retrieval (ER), i.e., retrieving examples most similar to aninput expression, is the most dominant part of the total processing time. | |
| tech,27-3-A94-1017,ak | We have already proposed a model , <term> TDMT ( Transfer-Driven Machine Translation ) </term> , that translates a <term> sentence </term> utilizing <term> examples </term> effectively and performs accurate <term> structural disambiguation </term> and <term> target word selection </term> . | #26140 We have already proposed a model, TDMT (Transfer-Driven Machine Translation), that translates a sentence utilizing examples effectively and performs accurate structural disambiguation andtarget word selection. | |
| tech,7-8-A94-1017,ak | Moreover , a study on communications between <term> APs </term> demonstrates the <term> scalability </term> against <term> vocabulary size </term> by <term> extrapolation </term> . | #26223 Moreover, a study on communications betweenAPs demonstrates the scalability against vocabulary size by extrapolation. | |
| tech,15-8-A94-1017,ak | Moreover , a study on communications between <term> APs </term> demonstrates the <term> scalability </term> against <term> vocabulary size </term> by <term> extrapolation </term> . | #26231 Moreover, a study on communications between APs demonstrates the scalability against vocabulary size byextrapolation. | |
| tech,12-1-A94-1017,ak | This paper proposes a model using <term> associative processors ( APs ) </term> for <term> real-time spoken language translation </term> . | #26090 This paper proposes a model using associative processors (APs) forreal-time spoken language translation. | |
| other,13-6-A94-1017,ak | Our study has concluded that we only need to implement the <term> ER </term> for <term> expressions </term> including a <term> frequent word </term> on <term> APs </term> . | #26196 Our study has concluded that we only need to implement the ER forexpressions including a frequent word on APs. | |
| tech,1-5-A94-1017,ak | In <term> TDMT </term> , <term> example-retrieval ( ER ) </term> , i.e. , retrieving <term> examples </term> most similar to an <term> input expression </term> , is the most dominant part of the total processing time . | #26154 InTDMT, example-retrieval (ER), i.e., retrieving examples most similar to an input expression, is the most dominant part of the total processing time. | |
| tech,14-9-A94-1017,ak | Thus , our model , <term> TDMT </term> on <term> APs </term> , meets the vital requirements of <term> spoken language translation </term> . | #26247 Thus, our model, TDMT on APs, meets the vital requirements ofspoken language translation. | |
| tech,7-9-A94-1017,ak | Thus , our model , <term> TDMT </term> on <term> APs </term> , meets the vital requirements of <term> spoken language translation </term> . | #26240 Thus, our model, TDMT onAPs, meets the vital requirements of spoken language translation. | |
| tech,19-6-A94-1017,ak | Our study has concluded that we only need to implement the <term> ER </term> for <term> expressions </term> including a <term> frequent word </term> on <term> APs </term> . | #26202 Our study has concluded that we only need to implement the ER for expressions including a frequent word onAPs. | |
| model,11-5-A94-1017,ak | In <term> TDMT </term> , <term> example-retrieval ( ER ) </term> , i.e. , retrieving <term> examples </term> most similar to an <term> input expression </term> , is the most dominant part of the total processing time . | #26164 In TDMT, example-retrieval (ER), i.e., retrievingexamples most similar to an input expression, is the most dominant part of the total processing time. |
