article considers approaches which rerank the output of an existing <term> probabilistic parser
other,28-1-H01-1042,bq human language learners </term> , to the <term> output </term> of <term> machine translation ( MT
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>
other,30-2-H05-2007,bq patterns </term> in <term> machine translation output </term> . Automatic <term> evaluation metrics
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
other,18-6-H01-1041,bq system </term> produces the <term> translation output </term> sufficient for content understanding
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