| tech,1-5-A94-1017,bq | concentrate on the second requirement . In <term> | TDMT | </term> , <term> example-retrieval ( ER ) </term> | #20269 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,3-5-A94-1017,bq | requirement . In <term> TDMT </term> , <term> | example-retrieval ( ER ) | </term> , i.e. , retrieving examples most | #20271 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,5-9-A94-1017,bq | extrapolation </term> . Thus , our model , <term> | TDMT on APs | </term> , meets the vital requirements of | #20353 Thus, our model,TDMT on APs, meets the vital requirements of spoken language translation. |
| tech,7-3-A94-1017,bq | We have already proposed a model , <term> | TDMT ( Transfer-Driven Machine Translation ) | </term> , that translates a <term> sentence | #20235 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,19-6-A94-1017,bq | including a frequent <term> word </term> on <term> | APs | </term> . Experimental results show that | #20317 Our study has concluded that we only need to implement the ER for expressions including a frequent word onAPs. |
| other,14-9-A94-1017,bq | </term> , meets the vital requirements of <term> | spoken language translation | </term> . <term> Japanese texts </term> frequently | #20362 Thus, our model, TDMT on APs, meets the vital requirements ofspoken language translation. |
| other,27-5-A94-1017,bq | the most dominant part of the total <term> | processing time | </term> . Our study has concluded that we | #20295 In TDMT, example-retrieval (ER), i.e., retrieving examples most similar to an input expression, is the most dominant part of the totalprocessing time. |
| other,12-1-A94-1017,bq | associative processors ( APs ) </term> for <term> | real-time spoken language translation | </term> . <term> Spoken language translation | #20205 This paper proposes a model using associative processors (APs) forreal-time spoken language translation. |
| tech,27-3-A94-1017,bq | structural disambiguation </term> and <term> | target word selection | </term> . This paper will concentrate on | #20255 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,15-8-A94-1017,bq | against <term> vocabulary size </term> by <term> | extrapolation | </term> . Thus , our model , <term> TDMT on | #20346 Moreover, a study on communications between APs demonstrates the scalability against vocabulary size byextrapolation. |
| other,15-2-A94-1017,bq | <term> translation </term> and ( 2 ) a <term> | real-time response | </term> . We have already proposed a model | #20225 Spoken language translation requires (1) an accurate translation and (2) areal-time response. |
| other,10-8-A94-1017,bq | between <term> APs </term> demonstrates the <term> | scalability | </term> against <term> vocabulary size </term> | #20341 Moreover, a study on communications between APs demonstrates thescalability against vocabulary size by extrapolation. |
| other,9-2-A94-1017,bq | translation </term> requires ( 1 ) an accurate <term> | translation | </term> and ( 2 ) a <term> real-time response | #20219 Spoken language translation requires (1) an accuratetranslation and (2) a real-time response. |
| tech,24-3-A94-1017,bq | effectively and performs accurate <term> | structural disambiguation | </term> and <term> target word selection </term> | #20252 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,bq | the <term> scalability </term> against <term> | vocabulary size | </term> by <term> extrapolation </term> . Thus | #20343 Moreover, a study on communications between APs demonstrates the scalability againstvocabulary size by extrapolation. |
| tech,5-7-A94-1017,bq | Experimental results show that the <term> | ER | </term> can be drastically speeded up . Moreover | #20324 Experimental results show that theER can be drastically speeded up. |
| tech,7-8-A94-1017,bq | a study on communications between <term> | APs | </term> demonstrates the <term> scalability | #20338 Moreover, a study on communications betweenAPs demonstrates the scalability against vocabulary size by extrapolation. |
| tech,11-6-A94-1017,bq | that we only need to implement the <term> | ER | </term> for <term> expressions </term> including | #20309 Our study has concluded that we only need to implement theER for expressions including a frequent word on APs. |
| tech,6-1-A94-1017,bq | This paper proposes a model using <term> | associative processors ( APs ) | </term> for <term> real-time spoken language | #20199 This paper proposes a model usingassociative processors ( APs ) for real-time spoken language translation. |
| other,13-6-A94-1017,bq | to implement the <term> ER </term> for <term> | expressions | </term> including a frequent <term> word </term> | #20311 Our study has concluded that we only need to implement the ER forexpressions including a frequent word on APs. |
