| other,1-4-H01-1042,bq | </term> of <term> MT output </term> . A <term> | language learning experiment | </term> showed that <term> assessors </term> | #629 Alanguage learning experiment showed that assessors can differentiate native from non-native language essays in less than 100 words. |
| other,11-7-H01-1042,bq | given a set of up to six extracts of <term> | translated newswire text | </term> . Some of the extracts were <term> | #693 Subjects were given a set of up to six extracts oftranslated newswire text. |
| other,11-8-H01-1042,bq | human translations </term> , others were <term> | machine translation outputs | </term> . The subjects were given three minutes | #708 Some of the extracts were expert human translations, others weremachine translation outputs. |
| other,12-2-H01-1042,bq | provide information about both the <term> | human language learning process | </term> , the <term> translation process </term> | #593 We believe that these evaluation techniques will provide information about both thehuman language learning process, the translation process and the development of machine translation systems. |
| other,14-3-H01-1042,bq | series of experiments , looks at the <term> | intelligibility | </term> of <term> MT output </term> . A <term> | #623 This, the first experiment in a series of experiments, looks at theintelligibility of MT output. |
| other,16-3-H01-1042,bq | the <term> intelligibility </term> of <term> | MT output | </term> . A <term> language learning experiment | #625 This, the first experiment in a series of experiments, looks at the intelligibility ofMT output. |
| other,16-6-H01-1042,bq | from duplicating the experiment using <term> | machine translation output | </term> . Subjects were given a set of up | #678 We tested this to see if similar criteria could be elicited from duplicating the experiment usingmachine translation output. |
| other,18-2-H01-1042,bq | language learning process </term> , the <term> | translation process | </term> and the <term> development </term> of | #599 We believe that these evaluation techniques will provide information about both the human language learning process, thetranslation process and the development of machine translation systems. |
| other,18-4-H01-1042,bq | language essays </term> in less than 100 <term> | words | </term> . Even more illuminating was the | #646 A language learning experiment showed that assessors can differentiate native from non-native language essays in less than 100words. |
| other,19-9-H01-1042,bq | believed the sample output to be an <term> | expert human translation | </term> or a <term> machine translation </term> | #731 The subjects were given three minutes per extract to determine whether they believed the sample output to be anexpert human translation or a machine translation. |
| other,22-1-H01-1042,bq | devised for the <term> evaluation </term> of <term> | human language learners | </term> , to the <term> output </term> of <term> | #566 The purpose of this research is to test the efficacy of applying automated evaluation techniques, originally devised for the evaluation ofhuman language learners, to the output of machine translation (MT) systems. |
| other,24-9-H01-1042,bq | expert human translation </term> or a <term> | machine translation | </term> . Additionally , they were asked | #736 The subjects were given three minutes per extract to determine whether they believed the sample output to be an expert human translation or amachine translation. |
| other,28-1-H01-1042,bq | human language learners </term> , to the <term> | output | </term> of <term> machine translation ( MT | #572 The purpose of this research is to test the efficacy of applying automated evaluation techniques, originally devised for the evaluation of human language learners, to theoutput of machine translation (MT) systems. |
| other,5-8-H01-1042,bq | </term> . Some of the extracts were <term> | expert human translations | </term> , others were <term> machine translation | #702 Some of the extracts wereexpert human translations, others were machine translation outputs. |
| other,6-4-H01-1042,bq | learning experiment </term> showed that <term> | assessors | </term> can differentiate <term> native from | #634 A language learning experiment showed thatassessors can differentiate native from non-native language essays in less than 100 words. |
| other,8-10-H01-1042,bq | Additionally , they were asked to mark the <term> | word | </term> at which they made this decision | #747 Additionally, they were asked to mark theword at which they made this decision. |
| other,9-4-H01-1042,bq | <term> assessors </term> can differentiate <term> | native from non-native language essays | </term> in less than 100 <term> words </term> | #637 A language learning experiment showed that assessors can differentiatenative from non-native language essays in less than 100 words. |
| other,9-5-H01-1042,bq | illuminating was the factors on which the <term> | assessors | </term> made their decisions . We tested | #657 Even more illuminating was the factors on which theassessors made their decisions. |
| tech,12-1-H01-1042,bq | is to test the efficacy of applying <term> | automated evaluation techniques | </term> , originally devised for the <term> | #556 The purpose of this research is to test the efficacy of applyingautomated evaluation techniques, originally devised for the evaluation of human language learners, to the output of machine translation (MT) systems. |
| tech,20-1-H01-1042,bq | </term> , originally devised for the <term> | evaluation | </term> of <term> human language learners </term> | #564 The purpose of this research is to test the efficacy of applying automated evaluation techniques, originally devised for theevaluation of human language learners, to the output of machine translation (MT) systems. |
