#646A language learning experiment showed that assessors can differentiate native from non-native language essays in less than 100words.
other,28-1-H01-1042,ak
human language learners
</term>
, to the
<term>
output
</term>
of
<term>
machine translation ( MT
#572The 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,18-2-H01-1042,ak
language learning process
</term>
, the
<term>
translation process
</term>
and the development of
<term>
machine
#599We 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,6-4-H01-1042,ak
learning experiment
</term>
showed that
<term>
assessors
</term>
can differentiate
<term>
native from
#634A language learning experiment showed thatassessors can differentiate native from non-native language essays in less than 100 words.
tech,12-1-H01-1042,ak
is to test the efficacy of applying
<term>
automated evaluation techniques
</term>
, originally devised for the
<term>
#556The 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.
other,24-9-H01-1042,ak
expert human translation
</term>
or a
<term>
machine translation
</term>
. Additionally , they were asked
#736The 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,11-8-H01-1042,ak
human translations
</term>
, others were
<term>
machine translation outputs
</term>
. The subjects were given three minutes
#708Some of the extracts were expert human translations, others weremachine translation outputs.
other,16-3-H01-1042,ak
the
<term>
intelligibility
</term>
of
<term>
MT output
</term>
. A
<term>
language learning experiment
#625This, the first experiment in a series of experiments, looks at the intelligibility ofMT output.
other,5-8-H01-1042,ak
newswire text . Some of the extracts were
<term>
expert human translations
</term>
, others were
<term>
machine translation
#702Some of the extracts wereexpert human translations, others were machine translation outputs.
other,14-3-H01-1042,ak
series of experiments , looks at the
<term>
intelligibility
</term>
of
<term>
MT output
</term>
. A
<term>
#623This, the first experiment in a series of experiments, looks at theintelligibility of MT output.
other,9-5-H01-1042,ak
illuminating was the factors on which the
<term>
assessors
</term>
made their decisions . We tested
#657Even more illuminating was the factors on which theassessors made their decisions.
tech,20-1-H01-1042,ak
</term>
, originally devised for the
<term>
evaluation
</term>
of
<term>
human language learners
</term>
#564The 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.
tech,24-2-H01-1042,ak
process
</term>
and the development of
<term>
machine translation systems
</term>
. This , the first experiment in
#605We believe that these evaluation techniques will provide information about both the human language learning process, the translation process and the development ofmachine translation systems.
other,1-4-H01-1042,ak
</term>
of
<term>
MT output
</term>
. A
<term>
language learning experiment
</term>
showed that
<term>
assessors
</term>
#629Alanguage learning experiment showed that assessors can differentiate native from non-native language essays in less than 100 words.
tech,30-1-H01-1042,ak
</term>
, to the
<term>
output
</term>
of
<term>
machine translation ( MT ) systems
</term>
. We believe that these
<term>
evaluation
#574The purpose of this research is to test the efficacy of applying automated evaluation techniques, originally devised for the evaluation of human language learners, to the output ofmachine translation ( MT ) systems.
other,19-9-H01-1042,ak
believed the sample output to be an
<term>
expert human translation
</term>
or a
<term>
machine translation
</term>
#731The subjects were given three minutes per extract to determine whether they believed the sample output to be anexpert human translation or a machine translation.
tech,4-2-H01-1042,ak
systems
</term>
. We believe that these
<term>
evaluation techniques
</term>
will provide information about both
#585We believe that theseevaluation techniques will provide information about both the human language learning process, the translation process and the development of machine translation systems.
other,9-4-H01-1042,ak
<term>
assessors
</term>
can differentiate
<term>
native from non-native language essays
</term>
in less than 100
<term>
words
</term>
#637A language learning experiment showed that assessors can differentiatenative from non-native language essays in less than 100 words.
other,22-1-H01-1042,ak
devised for the
<term>
evaluation
</term>
of
<term>
human language learners
</term>
, to the
<term>
output
</term>
of
<term>
#566The 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,12-2-H01-1042,ak
provide information about both the
<term>
human language learning process
</term>
, the
<term>
translation process
</term>
#593We believe that these evaluation techniques will provide information about both thehuman language learning process, the translation process and the development of machine translation systems.