other,0-4-H05-1095,bq <term> NIST evaluation metric </term> . <term> Translations </term> are produced by means of a <term> beam-search
tech,23-1-P01-1004,bq <term> retrieval performance </term> of a <term> translation memory system </term> . We take a selection
other,9-2-A94-1017,bq translation </term> requires ( 1 ) an accurate <term> translation </term> and ( 2 ) a <term> real-time response
tech,4-3-P01-1007,bq complexity </term> . For example , after <term> translation </term> into an equivalent <term> RCG </term>
tech,14-5-P05-1069,bq </term> on a standard <term> Arabic-English translation task </term> . Previous work has used <term>
other,22-2-J05-4003,bq determine whether or not they are <term> translations </term> of each other . Using this <term> approach
other,34-1-C90-3045,bq semantic interpretation </term> or <term> automatic translation of natural language </term> . We present
measure(ment),31-3-P05-1048,bq does not yield significantly better <term> translation quality </term> than the <term> statistical
measure(ment),17-8-P05-1067,bq the <term> IBM models </term> in both <term> translation speed and quality </term> . In this paper
tech,6-3-I05-2048,bq particularly important when building <term> translation systems </term> for new <term> language pairs
other,10-4-N04-1022,bq decoders </term> on a <term> Chinese-to-English translation task </term> . Our results show that <term>
other,10-3-P05-1048,bq disambiguation model </term> to choose <term> translation candidates </term> for a typical <term> IBM
this approach is that knowledge concerning translation equivalence of expressions may be directly
tech,16-1-C90-1013,bq generation </term> , developed for a <term> dialogue translation system </term> . The <term> system </term> utilizes
lr,12-2-P01-1008,bq </term> from a <term> corpus of multiple English translations </term> of the same <term> source text </term>
other,19-9-H01-1042,bq sample output to be an <term> expert human translation </term> or a <term> machine translation </term>
other,5-8-H01-1042,bq Some of the extracts were <term> expert human translations </term> , others were <term> machine translation
measure(ment),20-3-P05-1032,bq orders of magnitude with no loss in <term> translation quality </term> . We describe a novel <term>
lr,9-1-H05-2007,bq systematic <term> patterns </term> in <term> translation data </term> using <term> part-of-speech tag
tech,1-3-P84-1034,bq important roles . For <term> Japanese-English translation </term> , the <term> semantics directed approach
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