| other,11-8-H01-1042,bq | </term> , others were <term> machine translation | outputs | </term> . The subjects were given three minutes | #710 Some of the extracts were expert human translations, others were machine translation outputs. |
| tech,3-7-P05-1067,bq | <term> model </term> . We evaluate the <term> | outputs | </term> of our <term> MT system </term> using | #9512 We evaluate theoutputs of our MT system using the NIST and Bleu automatic MT evaluation software. |
| sentence-plan-ranker ( SPR ) </term> ranks the list of | output | <term> sentence plans </term> , and then selects | #1411 Second, the sentence-plan-ranker (SPR) ranks the list of output sentence plans, and then selects the top-ranked plan. | |
| tech,26-2-N03-1026,bq | maximum-entropy model </term> for <term> stochastic | output | selection </term> . Furthermore , we propose | #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. |
| </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. | |
| article considers approaches which rerank the | output | of an existing <term> probabilistic parser | #8656 This article considers approaches which rerank the output of an existing probabilistic parser. | |
| by gathering <term> statistics </term> on the | output | of other <term> linguistic tools </term> . | #16664 The scheme was implemented by gathering statistics on the output of other linguistic tools. | |
| determine whether they believed the sample | output | to be an <term> expert human 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. |
