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? |
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,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,3-3-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 .
|
#9350
Using a state-of-the-artChinese 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. |
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. |