tech,0-1-P05-1067,bq |
statistical MT architectures
</term>
.
<term>
|
Syntax-based statistical machine translation ( MT )
|
</term>
aims at applying
<term>
statistical
|
#9408
Error analysis suggests several key factors behind this surprising finding, including inherent limitations of current statistical MT architectures.Syntax-based statistical machine translation ( MT ) aims at applying statistical models to structured data. |
tech,10-1-P05-1067,bq |
translation ( MT )
</term>
aims at applying
<term>
|
statistical models
|
</term>
to
<term>
structured data
</term>
. In
|
#9418
Syntax-based statistical machine translation (MT) aims at applyingstatistical models to structured data. |
other,13-1-P05-1067,bq |
applying
<term>
statistical models
</term>
to
<term>
|
structured data
|
</term>
. In this paper , we present a
<term>
|
#9421
Syntax-based statistical machine translation (MT) aims at applying statistical models tostructured data. |
tech,7-2-P05-1067,bq |
</term>
. In this paper , we present a
<term>
|
syntax-based statistical machine translation system
|
</term>
based on a
<term>
probabilistic synchronous
|
#9431
In this paper, we present asyntax-based statistical machine translation system based on a probabilistic synchronous dependency insertion grammar. |
other,15-2-P05-1067,bq |
translation system
</term>
based on a
<term>
|
probabilistic synchronous dependency insertion grammar
|
</term>
.
<term>
Synchronous dependency insertion
|
#9439
In this paper, we present a syntax-based statistical machine translation system based on aprobabilistic synchronous dependency insertion grammar. |
other,0-3-P05-1067,bq |
dependency insertion grammar
</term>
.
<term>
|
Synchronous dependency insertion grammars
|
</term>
are a version of
<term>
synchronous
|
#9445
In this paper, we present a syntax-based statistical machine translation system based on a probabilistic synchronous dependency insertion grammar.Synchronous dependency insertion grammars are a version of synchronous grammars defined on dependency trees. |
other,8-3-P05-1067,bq |
insertion grammars
</term>
are a version of
<term>
|
synchronous grammars
|
</term>
defined on
<term>
dependency trees
</term>
|
#9453
Synchronous dependency insertion grammars are a version ofsynchronous grammars defined on dependency trees. |
other,12-3-P05-1067,bq |
synchronous grammars
</term>
defined on
<term>
|
dependency trees
|
</term>
. We first introduce our
<term>
approach
|
#9457
Synchronous dependency insertion grammars are a version of synchronous grammars defined ondependency trees. |
tech,4-4-P05-1067,bq |
trees
</term>
. We first introduce our
<term>
|
approach
|
</term>
to inducing such a
<term>
grammar
</term>
|
#9464
We first introduce ourapproach to inducing such a grammar from parallel corpora. |
other,9-4-P05-1067,bq |
<term>
approach
</term>
to inducing such a
<term>
|
grammar
|
</term>
from
<term>
parallel corpora
</term>
|
#9469
We first introduce our approach to inducing such agrammar from parallel corpora. |
lr,11-4-P05-1067,bq |
inducing such a
<term>
grammar
</term>
from
<term>
|
parallel corpora
|
</term>
. Second , we describe the
<term>
graphical
|
#9471
We first introduce our approach to inducing such a grammar fromparallel corpora. |
tech,5-5-P05-1067,bq |
corpora
</term>
. Second , we describe the
<term>
|
graphical model
|
</term>
for the
<term>
machine translation
|
#9479
Second, we describe thegraphical model for the machine translation task, which can also be viewed as a stochastic tree-to-tree transducer. |
other,9-5-P05-1067,bq |
<term>
graphical model
</term>
for the
<term>
|
machine translation task
|
</term>
, which can also be viewed as a
<term>
|
#9483
Second, we describe the graphical model for themachine translation task, which can also be viewed as a stochastic tree-to-tree transducer. |
tech,20-5-P05-1067,bq |
</term>
, which can also be viewed as a
<term>
|
stochastic tree-to-tree transducer
|
</term>
. We introduce a
<term>
polynomial
|
#9494
Second, we describe the graphical model for the machine translation task, which can also be viewed as astochastic tree-to-tree transducer. |
tech,3-6-P05-1067,bq |
transducer
</term>
. We introduce a
<term>
|
polynomial time decoding algorithm
|
</term>
for the
<term>
model
</term>
. We evaluate
|
#9501
We introduce apolynomial time decoding algorithm for the model. |
tech,9-6-P05-1067,bq |
time decoding algorithm
</term>
for the
<term>
|
model
|
</term>
. We evaluate the
<term>
outputs
</term>
|
#9507
We introduce a polynomial time decoding algorithm for themodel. |
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. |
tech,6-7-P05-1067,bq |
evaluate the
<term>
outputs
</term>
of our
<term>
|
MT system
|
</term>
using the
<term>
NIST and Bleu automatic
|
#9515
We evaluate the outputs of ourMT system using the NIST and Bleu automatic MT evaluation software. |
measure(ment),10-7-P05-1067,bq |
our
<term>
MT system
</term>
using the
<term>
|
NIST and Bleu automatic MT evaluation software
|
</term>
. The result shows that our
<term>
|
#9519
We evaluate the outputs of our MT system using theNIST and Bleu automatic MT evaluation software. |
tech,5-8-P05-1067,bq |
</term>
. The result shows that our
<term>
|
system
|
</term>
outperforms the
<term>
baseline system
|
#9532
The result shows that oursystem outperforms the baseline system based on the IBM models in both translation speed and quality. |