| other,18-1-P06-4014,bq | The <term> LOGON MT demonstrator </term> assembles independently valuable <term> general-purpose NLP components </term> into a <term> machine translation pipeline </term> that capitalizes on <term> output quality </term> . | #11807 The LOGON MT demonstrator assembles independently valuable general-purpose NLP components into a machine translation pipeline that capitalizes onoutput quality. |
| tech,26-2-N03-1026,bq | Our <term> system </term> incorporates a <term> linguistic parser/generator </term> for <term> LFG </term> , a <term> transfer component </term> for <term> parse reduction </term> operating on <term> packed parse forests </term> , and a <term> maximum-entropy model </term> for <term> stochastic output selection </term> . | #2835 Our system incorporates a linguistic parser/generator for LFG, a transfer component for parse reduction operating on packed parse forests, and a maximum-entropy model for stochastic output selection. |
| Second , the <term> sentence-plan-ranker ( SPR ) </term> ranks the list of output <term> sentence plans </term> , and then selects the top-ranked <term> plan </term> . | #1411 Second, the sentence-plan-ranker (SPR) ranks the list of output sentence plans, and then selects the top-ranked plan. | |
| other,18-6-H01-1041,bq | Having been trained on <term> Korean newspaper articles </term> on missiles and chemical biological warfare , the <term> system </term> produces the <term> translation output </term> sufficient for content understanding of the <term> original document </term> . | #534 Having been trained on Korean newspaper articles on missiles and chemical biological warfare, the system produces the translation output sufficient for content understanding of the original document. |
| We consider the case of <term> multi-document summarization </term> , where the input <term> documents </term> are in <term> Arabic </term> , and the output <term> summary </term> is in <term> English </term> . | #7170 We consider the case of multi-document summarization, where the input documents are in Arabic, and the output summary is in English. | |
| The subjects were given three minutes per extract to determine whether they believed the sample output to be an <term> expert human translation </term> or a <term> machine translation </term> . | #727 The subjects were given three minutes per extract to determine whether they believed the sample output to be an expert human translation or a machine translation. | |
| The use of <term> BLEU </term> at the <term> character </term> level eliminates the <term> word segmentation problem </term> : it makes it possible to directly compare commercial <term> systems </term> outputting <term> unsegmented texts </term> with , for instance , <term> statistical MT systems </term> which usually segment their <term> outputs </term> . | #7769 The use of BLEU at the character level eliminates the word segmentation problem: it makes it possible to directly compare commercial systemsoutputting unsegmented texts with, for instance, statistical MT systems which usually segment their outputs. | |
| other,12-4-P05-2016,bq | We also refer to an <term> evaluation method </term> and plan to compare our <term> system 's output </term> with a <term> benchmark system </term> . | #9825 We also refer to an evaluation method and plan to compare our system's output with a benchmark system. |
