other,24-2-C92-2115,bq to make decisions on the basis of <term> translation examples </term> at appropriate pints in <term>
tech,15-2-A94-1007,bq most difficult problems for <term> machine translation ( MT ) systems </term> . The problem is selecting
other,22-7-A94-1007,bq <term> accuracy </term> in the practical <term> translation </term> use . The use of <term> NLP techniques
other,12-1-A94-1017,bq </term> for <term> real-time spoken language translation </term> . <term> Spoken language translation
other,0-2-A94-1017,bq translation </term> . <term> Spoken language translation </term> requires ( 1 ) an accurate <term> translation
other,9-2-A94-1017,bq translation </term> requires ( 1 ) an accurate <term> translation </term> and ( 2 ) a <term> real-time response
tech,7-3-A94-1017,bq model , <term> TDMT ( Transfer-Driven Machine Translation ) </term> , that translates a <term> sentence
other,14-9-A94-1017,bq vital requirements of <term> spoken language translation </term> . <term> Japanese texts </term> frequently
other,28-1-C94-1052,bq <term> lexical entries </term> to their <term> translation equivalents </term> . To help this task we
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