other,11-8-H01-1042,bq </term> , others were <term> machine translation outputs </term> . The subjects were given three minutes
tech,3-7-P05-1067,bq <term> model </term> . We evaluate the <term> outputs </term> of our <term> MT system </term> using
sentence-plan-ranker ( SPR ) </term> ranks the list of output <term> sentence plans </term> , and then selects
tech,26-2-N03-1026,bq maximum-entropy model </term> for <term> stochastic output selection </term> . Furthermore , we propose
</term> are in <term> Arabic </term> , and the output <term> summary </term> is in <term> English </term>
article considers approaches which rerank the output of an existing <term> probabilistic parser
by gathering <term> statistics </term> on the output of other <term> linguistic tools </term> .
determine whether they believed the sample output to be an <term> expert human translation </term>
hide detail