#9184We directly investigate a subject of much recent debate: do word sense disambigation models helpstatistical machine translation quality?
measure(ment),31-3-P05-1048,ak
does not yield significantly better
<term>
translation quality
</term>
than the
<term>
statistical machine
#9235Using 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.
model,11-1-P05-1048,ak
subject of much recent debate : do
<term>
word sense disambigation models
</term>
help
<term>
statistical machine translation
#9179We directly investigate a subject of much recent debate: doword sense disambigation models help statistical machine translation quality?
model,3-3-P05-1048,ak
assumption . Using a state-of-the-art
<term>
Chinese word sense disambiguation model
</term>
to choose
<term>
translation candidates
#9207Using 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,ak
disambiguation model
</term>
to choose
<term>
translation candidates
</term>
for a typical
<term>
IBM statistical
#9214Using 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.
#9261Error analysis suggests several key factors behind this surprising finding, including inherent limitations of currentstatistical MT architectures.
tech,0-4-P05-1048,ak
machine translation system
</term>
alone .
<term>
Error analysis
</term>
suggests several key factors behind
#9245Using 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,15-3-P05-1048,ak
translation candidates
</term>
for a typical
<term>
IBM statistical MT system
</term>
, we find that
<term>
word sense disambiguation
#9219Using 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,ak
statistical MT system
</term>
, we find that
<term>
word sense disambiguation
</term>
does not yield significantly better
#9227Using 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,ak
translation quality
</term>
than the
<term>
statistical machine translation system
</term>
alone .
<term>
Error analysis
</term>
#9239Using 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.