other,10-2-I05-2014,bq |
scarcely used for the assessment of
<term>
|
language pairs
|
</term>
like
<term>
English-Chinese
</term>
or
|
#7710
Yet, they are scarcely used for the assessment oflanguage pairs like English-Chinese or English-Japanese, because of the word segmentation problem. |
measure(ment),3-4-I05-2014,bq |
character
</term>
level . The use of
<term>
|
BLEU
|
</term>
at the
<term>
character
</term>
level
|
#7749
The use ofBLEU at the character level eliminates the word segmentation problem: it makes it possible to directly compare commercial systems outputting unsegmented texts with, for instance, statistical MT systems which usually segment their outputs. |
other,24-4-I05-2014,bq |
commercial
<term>
systems
</term>
outputting
<term>
|
unsegmented texts
|
</term>
with , for instance ,
<term>
statistical
|
#7770
The use of BLEU at the character level eliminates the word segmentation problem: it makes it possible to directly compare commercial systems outputtingunsegmented texts with, for instance, statistical MT systems which usually segment their outputs. |
tech,22-4-I05-2014,bq |
possible to directly compare commercial
<term>
|
systems
|
</term>
outputting
<term>
unsegmented texts
|
#7768
The use of BLEU at the character level eliminates the word segmentation problem: it makes it possible to directly compare commercialsystems outputting unsegmented texts with, for instance, statistical MT systems which usually segment their outputs. |
tech,31-4-I05-2014,bq |
texts
</term>
with , for instance ,
<term>
|
statistical MT systems
|
</term>
which usually segment their
<term>
|
#7777
The use of BLEU at the character level eliminates the word segmentation problem: it makes it possible to directly compare commercial systems outputting unsegmented texts with, for instance,statistical MT systems which usually segment their outputs. |
other,38-4-I05-2014,bq |
</term>
which usually segment their
<term>
|
outputs
|
</term>
. We present the first known
<term>
|
#7784
The use of BLEU at the character level eliminates the word segmentation problem: it makes it possible to directly compare commercial systems outputting unsegmented texts with, for instance, statistical MT systems which usually segment theiroutputs. |
tech,4-1-I05-2014,bq |
<term>
evaluation metrics
</term>
for
<term>
|
Machine Translation ( MT ) systems
|
</term>
, such as
<term>
BLEU
</term>
or
<term>
|
#7682
Automatic evaluation metrics forMachine Translation ( MT ) systems, such as BLEU or NIST, are now well established. |
other,15-2-I05-2014,bq |
like
<term>
English-Chinese
</term>
or
<term>
|
English-Japanese
|
</term>
, because of the
<term>
word segmentation
|
#7715
Yet, they are scarcely used for the assessment of language pairs like English-Chinese orEnglish-Japanese, because of the word segmentation problem. |
other,12-3-I05-2014,bq |
standard use of
<term>
BLEU
</term>
in
<term>
|
word n-grams
|
</term>
and its application at the
<term>
character
|
#7736
This study establishes the equivalence between the standard use of BLEU inword n-grams and its application at the character level. |
measure(ment),15-1-I05-2014,bq |
</term>
, such as
<term>
BLEU
</term>
or
<term>
|
NIST
|
</term>
, are now well established . Yet
|
#7693
Automatic evaluation metrics for Machine Translation (MT) systems, such as BLEU orNIST, are now well established. |
other,20-2-I05-2014,bq |
English-Japanese
</term>
, because of the
<term>
|
word segmentation problem
|
</term>
. This study establishes the equivalence
|
#7720
Yet, they are scarcely used for the assessment of language pairs like English-Chinese or English-Japanese, because of theword segmentation problem. |
measure(ment),10-3-I05-2014,bq |
equivalence between the standard use of
<term>
|
BLEU
|
</term>
in
<term>
word n-grams
</term>
and its
|
#7734
This study establishes the equivalence between the standard use ofBLEU in word n-grams and its application at the character level. |
measure(ment),13-1-I05-2014,bq |
Translation ( MT ) systems
</term>
, such as
<term>
|
BLEU
|
</term>
or
<term>
NIST
</term>
, are now well
|
#7691
Automatic evaluation metrics for Machine Translation (MT) systems, such asBLEU or NIST, are now well established. |
measure(ment),1-1-I05-2014,bq |
translation output
</term>
. Automatic
<term>
|
evaluation metrics
|
</term>
for
<term>
Machine Translation ( MT
|
#7679
Automaticevaluation metrics for Machine Translation (MT) systems, such as BLEU or NIST, are now well established. |
other,13-2-I05-2014,bq |
of
<term>
language pairs
</term>
like
<term>
|
English-Chinese
|
</term>
or
<term>
English-Japanese
</term>
,
|
#7713
Yet, they are scarcely used for the assessment of language pairs likeEnglish-Chinese or English-Japanese, because of the word segmentation problem. |
other,10-4-I05-2014,bq |
character
</term>
level eliminates the
<term>
|
word segmentation problem
|
</term>
: it makes it possible to directly
|
#7756
The use of BLEU at the character level eliminates theword segmentation problem: it makes it possible to directly compare commercial systems outputting unsegmented texts with, for instance, statistical MT systems which usually segment their outputs. |