tech,11-1-P05-1048,bq |
subject of much recent debate : do
<term>
|
word sense disambigation models
|
</term>
help
<term>
statistical machine translation
|
#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 |
sense disambigation models
</term>
help
<term>
|
statistical machine translation
|
</term><term>
quality
</term>
? We present
|
#9327
We directly investigate a subject of much recent debate: do word sense disambigation models helpstatistical machine translation quality? |
measure(ment),19-1-P05-1048,bq |
statistical machine translation
</term><term>
|
quality
|
</term>
? We present empirical results casting
|
#9330
We directly investigate a subject of much recent debate: do word sense disambigation models help statistical machine translationquality? |
tech,3-3-P05-1048,bq |
assumption . Using a state-of-the-art
<term>
|
Chinese word sense disambiguation model
|
</term>
to choose
<term>
translation candidates
|
#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. |
other,10-3-P05-1048,bq |
disambiguation model
</term>
to choose
<term>
|
translation candidates
|
</term>
for a typical
<term>
IBM statistical
|
#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. |
tool,15-3-P05-1048,bq |
translation candidates
</term>
for a typical
<term>
|
IBM statistical MT system
|
</term>
, we find that
<term>
word sense disambiguation
|
#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. |
tech,23-3-P05-1048,bq |
statistical MT system
</term>
, we find that
<term>
|
word sense disambiguation
|
</term>
does not yield significantly better
|
#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. |
other,16-4-P05-1048,bq |
including inherent limitations of current
<term>
|
statistical MT architectures
|
</term>
.
<term>
Syntax-based statistical machine
|
#9404
Error analysis suggests several key factors behind this surprising finding, including inherent limitations of currentstatistical MT architectures. |