tech,14-5-P05-1069,bq |
<term>
baseline
</term>
on a standard
<term>
|
Arabic-English translation task
|
</term>
. Previous work has used
<term>
monolingual
|
#9650
The best system obtains a 18.6% improvement over the baseline on a standardArabic-English translation task. |
other,8-4-P05-1069,bq |
</term>
can easily handle millions of
<term>
|
features
|
</term>
. The best system obtains a 18.6
|
#9634
Our training algorithm can easily handle millions offeatures. |
tech,17-1-P05-1069,bq |
phrase-based prediction model
</term>
for
<term>
|
statistical machine translation ( SMT )
|
</term>
. The
<term>
model
</term>
predicts
<term>
|
#9566
In this paper, we present a novel training method for a localized phrase-based prediction model forstatistical machine translation ( SMT ). |
other,8-2-P05-1069,bq |
blocks
</term>
with orientation to handle
<term>
|
local phrase re-ordering
|
</term>
. We use a
<term>
maximum likelihood
|
#9581
The model predicts blocks with orientation to handlelocal phrase re-ordering. |
other,15-3-P05-1069,bq |
block bigram model
</term>
which uses
<term>
|
real-valued features
|
</term>
( e.g. a
<term>
language model score
|
#9600
We use a maximum likelihood criterion to train a log-linear block bigram model which usesreal-valued features (e.g. a language model score) as well as binary features based on the block identities themselves, e.g. block bigram features. |
other,20-3-P05-1069,bq |
real-valued features
</term>
( e.g. a
<term>
|
language model score
|
</term>
) as well as
<term>
binary features
|
#9605
We use a maximum likelihood criterion to train a log-linear block bigram model which uses real-valued features (e.g. alanguage model score) as well as binary features based on the block identities themselves, e.g. block bigram features. |
other,27-3-P05-1069,bq |
language model score
</term>
) as well as
<term>
|
binary features
|
</term>
based on the
<term>
block
</term>
identities
|
#9612
We use a maximum likelihood criterion to train a log-linear block bigram model which uses real-valued features (e.g. a language model score) as well asbinary features based on the block identities themselves, e.g. block bigram features. |
tech,1-4-P05-1069,bq |
, e.g. block bigram features . Our
<term>
|
training algorithm
|
</term>
can easily handle millions of
<term>
|
#9627
Ourtraining algorithm can easily handle millions of features. |
tech,8-1-P05-1069,bq |
In this paper , we present a novel
<term>
|
training method
|
</term>
for a
<term>
localized phrase-based
|
#9557
In this paper, we present a noveltraining method for a localized phrase-based prediction model for statistical machine translation (SMT). |
model,12-1-P05-1069,bq |
novel
<term>
training method
</term>
for a
<term>
|
localized phrase-based prediction model
|
</term>
for
<term>
statistical machine translation
|
#9561
In this paper, we present a novel training method for alocalized phrase-based prediction model for statistical machine translation (SMT). |
other,32-3-P05-1069,bq |
binary features
</term>
based on the
<term>
|
block
|
</term>
identities themselves , e.g. block
|
#9617
We use a maximum likelihood criterion to train a log-linear block bigram model which uses real-valued features (e.g. a language model score) as well as binary features based on theblock identities themselves, e.g. block bigram features. |
measure(ment),10-5-P05-1069,bq |
obtains a 18.6 % improvement over the
<term>
|
baseline
|
</term>
on a standard
<term>
Arabic-English
|
#9646
The best system obtains a 18.6% improvement over thebaseline on a standard Arabic-English translation task. |
model,1-2-P05-1069,bq |
machine translation ( SMT )
</term>
. The
<term>
|
model
|
</term>
predicts
<term>
blocks
</term>
with orientation
|
#9574
Themodel predicts blocks with orientation to handle local phrase re-ordering. |
other,3-3-P05-1069,bq |
phrase re-ordering
</term>
. We use a
<term>
|
maximum likelihood criterion
|
</term>
to train a
<term>
log-linear block
|
#9588
We use amaximum likelihood criterion to train a log-linear block bigram model which uses real-valued features (e.g. a language model score) as well as binary features based on the block identities themselves, e.g. block bigram features. |
model,9-3-P05-1069,bq |
likelihood criterion
</term>
to train a
<term>
|
log-linear block bigram model
|
</term>
which uses
<term>
real-valued features
|
#9594
We use a maximum likelihood criterion to train alog-linear block bigram model which uses real-valued features (e.g. a language model score) as well as binary features based on the block identities themselves, e.g. block bigram features. |
other,3-2-P05-1069,bq |
</term>
. The
<term>
model
</term>
predicts
<term>
|
blocks
|
</term>
with orientation to handle
<term>
local
|
#9576
The model predictsblocks with orientation to handle local phrase re-ordering. |