. In this paper we show how two standard outputs from <term> information extraction ( IE )
other,18-6-H01-1041,bq system </term> produces the <term> translation output </term> sufficient for content understanding
other,28-1-H01-1042,bq human language learners </term> , to the <term> output </term> of <term> machine translation ( MT
other,16-3-H01-1042,bq the <term> intelligibility </term> of <term> MT output </term> . A <term> language learning experiment
other,16-6-H01-1042,bq experiment using <term> machine translation output </term> . Subjects were given a set of up
other,11-8-H01-1042,bq </term> , others were <term> machine translation outputs </term> . The subjects were given three minutes
determine whether they believed the sample output to be an <term> expert human translation </term>
sentence-plan-ranker ( SPR ) </term> ranks the list of output <term> sentence plans </term> , and then selects
the <term> shared derivation forest </term> output by a prior <term> RCL parser </term> for a
other,21-3-N03-1001,bq particular <term> domain </term> ; the <term> output </term> of <term> recognition </term> with this
other,38-1-N03-1018,bq through its transformation into the <term> noisy output </term> of an <term> OCR system </term> . The
other,16-2-N03-1018,bq on <term> post-processing </term> the <term> output </term> of black-box <term> OCR systems </term>
tech,26-2-N03-1026,bq maximum-entropy model </term> for <term> stochastic output selection </term> . Furthermore , we propose
other,15-5-N03-1026,bq <term> grammaticality </term> of the <term> system output </term> due to the use of a <term> constraint-based
</term> are in <term> Arabic </term> , and the output <term> summary </term> is in <term> English </term>
measure(ment),6-3-H05-1117,bq <term> methods </term> for <term> scoring system output </term> is an impediment to progress in the
other,30-2-H05-2007,bq patterns </term> in <term> machine translation output </term> . Automatic <term> evaluation metrics
directly compare commercial <term> systems </term> outputting <term> unsegmented texts </term> with , for
other,38-4-I05-2014,bq </term> which usually segment their <term> outputs </term> . We present the first known <term>
other,11-3-I05-6011,bq </term> and improving <term> machine translation outputs </term> . Annotating <term> honorifics </term>
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