tech,4-1-P05-1034,bq |
quality
</term>
. We describe a novel
<term>
|
approach
|
</term>
to
<term>
statistical machine translation
|
#9205
We describe a novelapproach to statistical machine translation that combines syntactic information in the source language with recent advances in phrasal translation. |
tech,4-4-P05-1034,bq |
model
</term>
. We describe an efficient
<term>
|
decoder
|
</term>
and show that using these
<term>
tree-based
|
#9277
We describe an efficientdecoder and show that using these tree-based models in combination with conventional SMT models provides a promising approach that incorporates the power of phrasal SMT with the linguistic generality available in a parser. |
tech,5-2-P05-1034,bq |
requires a
<term>
source-language
</term><term>
|
dependency parser
|
</term>
,
<term>
target language
</term><term>
|
#9230
This method requires a source-languagedependency parser, target language word segmentation and an unsupervised word alignment component. |
other,17-3-P05-1034,bq |
target
<term>
sentence
</term>
, extract
<term>
|
dependency treelet translation pairs
|
</term>
, and train a
<term>
tree-based ordering
|
#9261
We align a parallel corpus, project the source dependency parse onto the target sentence, extractdependency treelet translation pairs, and train a tree-based ordering model. |
tech,1-2-P05-1034,bq |
<term>
phrasal translation
</term>
. This
<term>
|
method
|
</term>
requires a
<term>
source-language
</term>
|
#9226
Thismethod requires a source-language dependency parser, target language word segmentation and an unsupervised word alignment component. |
lr,3-3-P05-1034,bq |
alignment component
</term>
. We align a
<term>
|
parallel corpus
|
</term>
, project the
<term>
source dependency
|
#9247
We align aparallel corpus, project the source dependency parse onto the target sentence, extract dependency treelet translation pairs, and train a tree-based ordering model. |
tech,36-4-P05-1034,bq |
linguistic generality available in a
<term>
|
parser
|
</term>
. We directly investigate a subject
|
#9309
We describe an efficient decoder and show that using these tree-based models in combination with conventional SMT models provides a promising approach that incorporates the power of phrasal SMT with the linguistic generality available in aparser. |
tech,27-4-P05-1034,bq |
approach that incorporates the power of
<term>
|
phrasal SMT
|
</term>
with the linguistic generality available
|
#9300
We describe an efficient decoder and show that using these tree-based models in combination with conventional SMT models provides a promising approach that incorporates the power ofphrasal SMT with the linguistic generality available in a parser. |
other,21-1-P05-1034,bq |
language
</term>
with recent advances in
<term>
|
phrasal translation
|
</term>
. This
<term>
method
</term>
requires
|
#9222
We describe a novel approach to statistical machine translation that combines syntactic information in the source language with recent advances inphrasal translation. |
other,14-3-P05-1034,bq |
dependency parse
</term>
onto the target
<term>
|
sentence
|
</term>
, extract
<term>
dependency treelet
|
#9258
We align a parallel corpus, project the source dependency parse onto the targetsentence, extract dependency treelet translation pairs, and train a tree-based ordering model. |
model,16-4-P05-1034,bq |
</term>
in combination with conventional
<term>
|
SMT models
|
</term>
provides a promising approach that
|
#9289
We describe an efficient decoder and show that using these tree-based models in combination with conventionalSMT models provides a promising approach that incorporates the power of phrasal SMT with the linguistic generality available in a parser. |
other,8-3-P05-1034,bq |
parallel corpus
</term>
, project the
<term>
|
source dependency parse
|
</term>
onto the target
<term>
sentence
</term>
|
#9252
We align a parallel corpus, project thesource dependency parse onto the target sentence, extract dependency treelet translation pairs, and train a tree-based ordering model. |
other,15-1-P05-1034,bq |
syntactic information
</term>
in the
<term>
|
source language
|
</term>
with recent advances in
<term>
phrasal
|
#9216
We describe a novel approach to statistical machine translation that combines syntactic information in thesource language with recent advances in phrasal translation. |
other,4-2-P05-1034,bq |
This
<term>
method
</term>
requires a
<term>
|
source-language
|
</term><term>
dependency parser
</term>
,
<term>
|
#9229
This method requires asource-language dependency parser, target language word segmentation and an unsupervised word alignment component. |
tech,6-1-P05-1034,bq |
describe a novel
<term>
approach
</term>
to
<term>
|
statistical machine translation
|
</term>
that combines
<term>
syntactic information
|
#9207
We describe a novel approach tostatistical machine translation that combines syntactic information in the source language with recent advances in phrasal translation. |
other,11-1-P05-1034,bq |
machine translation
</term>
that combines
<term>
|
syntactic information
|
</term>
in the
<term>
source language
</term>
|
#9212
We describe a novel approach to statistical machine translation that combinessyntactic information in the source language with recent advances in phrasal translation. |
other,8-2-P05-1034,bq |
</term><term>
dependency parser
</term>
,
<term>
|
target language
|
</term><term>
word segmentation
</term>
and
|
#9233
This method requires a source-language dependency parser,target language word segmentation and an unsupervised word alignment component. |
model,10-4-P05-1034,bq |
decoder
</term>
and show that using these
<term>
|
tree-based models
|
</term>
in combination with conventional
<term>
|
#9283
We describe an efficient decoder and show that using thesetree-based models in combination with conventional SMT models provides a promising approach that incorporates the power of phrasal SMT with the linguistic generality available in a parser. |
model,25-3-P05-1034,bq |
translation pairs
</term>
, and train a
<term>
|
tree-based ordering model
|
</term>
. We describe an efficient
<term>
decoder
|
#9269
We align a parallel corpus, project the source dependency parse onto the target sentence, extract dependency treelet translation pairs, and train atree-based ordering model. |
tech,14-2-P05-1034,bq |
<term>
word segmentation
</term>
and an
<term>
|
unsupervised word alignment component
|
</term>
. We align a
<term>
parallel corpus
|
#9239
This method requires a source-language dependency parser, target language word segmentation and anunsupervised word alignment component. |