measure(ment),19-1-P05-1048,bq |
We directly investigate a subject of much recent debate : do
<term>
word sense disambigation models
</term>
help
<term>
statistical machine translation
</term><term>
quality
</term>
?
|
#9330
We directly investigate a subject of much recent debate: do word sense disambigation models help statistical machine translationquality? |
measure(ment),31-3-P05-1048,bq |
Using a state-of-the-art
<term>
Chinese word sense disambiguation model
</term>
to choose
<term>
translation candidates
</term>
for a typical
<term>
IBM statistical MT system
</term>
, we find that
<term>
word sense disambiguation
</term>
does not yield significantly better
<term>
translation quality
</term>
than the
<term>
statistical machine translation system
</term>
alone .
|
#9378
Using a state-of-the-art Chinese word sense disambiguation model to choose translation candidates for a typical IBM statistical MT system, we find that word sense disambiguation does not yield significantly bettertranslation quality than the statistical machine translation system alone. |
other,10-3-P05-1048,bq |
Using a state-of-the-art
<term>
Chinese word sense disambiguation model
</term>
to choose
<term>
translation candidates
</term>
for a typical
<term>
IBM statistical MT system
</term>
, we find that
<term>
word sense disambiguation
</term>
does not yield significantly better
<term>
translation quality
</term>
than the
<term>
statistical machine translation system
</term>
alone .
|
#9357
Using a state-of-the-art Chinese word sense disambiguation model to choosetranslation candidates for a typical IBM statistical MT system, we find that word sense disambiguation does not yield significantly better translation quality than the statistical machine translation system alone. |
other,16-4-P05-1048,bq |
<term>
Error analysis
</term>
suggests several key factors behind this surprising finding , including inherent limitations of current
<term>
statistical MT architectures
</term>
.
|
#9404
Error analysis suggests several key factors behind this surprising finding, including inherent limitations of currentstatistical MT architectures. |
tech,0-4-P05-1048,bq |
Using a state-of-the-art
<term>
Chinese word sense disambiguation model
</term>
to choose
<term>
translation candidates
</term>
for a typical
<term>
IBM statistical MT system
</term>
, we find that
<term>
word sense disambiguation
</term>
does not yield significantly better
<term>
translation quality
</term>
than the
<term>
statistical machine translation system
</term>
alone .
<term>
Error analysis
</term>
suggests several key factors behind this surprising finding , including inherent limitations of current
<term>
statistical MT architectures
</term>
.
|
#9388
Using a state-of-the-art Chinese word sense disambiguation model to choose translation candidates for a typical IBM statistical MT system, we find that word sense disambiguation does not yield significantly better translation quality than the statistical machine translation system alone.Error analysis suggests several key factors behind this surprising finding, including inherent limitations of current statistical MT architectures. |
tech,11-1-P05-1048,bq |
We directly investigate a subject of much recent debate : do
<term>
word sense disambigation models
</term>
help
<term>
statistical machine translation
</term><term>
quality
</term>
?
|
#9322
We directly investigate a subject of much recent debate: doword sense disambigation models help statistical machine translation quality? |
tech,16-1-P05-1048,bq |
We directly investigate a subject of much recent debate : do
<term>
word sense disambigation models
</term>
help
<term>
statistical machine translation
</term><term>
quality
</term>
?
|
#9327
We directly investigate a subject of much recent debate: do word sense disambigation models helpstatistical machine translation quality? |
tech,23-3-P05-1048,bq |
Using a state-of-the-art
<term>
Chinese word sense disambiguation model
</term>
to choose
<term>
translation candidates
</term>
for a typical
<term>
IBM statistical MT system
</term>
, we find that
<term>
word sense disambiguation
</term>
does not yield significantly better
<term>
translation quality
</term>
than the
<term>
statistical machine translation system
</term>
alone .
|
#9370
Using a state-of-the-art Chinese word sense disambiguation model to choose translation candidates for a typical IBM statistical MT system, we find thatword sense disambiguation does not yield significantly better translation quality than the statistical machine translation system alone. |
tech,35-3-P05-1048,bq |
Using a state-of-the-art
<term>
Chinese word sense disambiguation model
</term>
to choose
<term>
translation candidates
</term>
for a typical
<term>
IBM statistical MT system
</term>
, we find that
<term>
word sense disambiguation
</term>
does not yield significantly better
<term>
translation quality
</term>
than the
<term>
statistical machine translation system
</term>
alone .
|
#9382
Using a state-of-the-art Chinese word sense disambiguation model to choose translation candidates for a typical IBM statistical MT system, we find that word sense disambiguation does not yield significantly better translation quality than thestatistical machine translation system alone. |
tool,15-3-P05-1048,bq |
Using a state-of-the-art
<term>
Chinese word sense disambiguation model
</term>
to choose
<term>
translation candidates
</term>
for a typical
<term>
IBM statistical MT system
</term>
, we find that
<term>
word sense disambiguation
</term>
does not yield significantly better
<term>
translation quality
</term>
than the
<term>
statistical machine translation system
</term>
alone .
|
#9362
Using a state-of-the-art Chinese word sense disambiguation model to choose translation candidates for a typicalIBM statistical MT system, we find that word sense disambiguation does not yield significantly better translation quality than the statistical machine translation system alone. |