Negative filter
Error, analysis 9
(288.1 per million)
model,11-1-P05-1048,ak
We directly investigate a subject of much recent debate : do
<term>
word sense disambigation models
</term>
help
<term>
statistical machine translation quality
</term>
?
#9179We directly investigate a subject of much recent debate: doword sense disambigation models help statistical machine translation quality?
measure(ment),16-1-P05-1048,ak
We directly investigate a subject of much recent debate : do
<term>
word sense disambigation models
</term>
help
<term>
statistical machine translation quality
</term>
?
#9184We directly investigate a subject of much recent debate: do word sense disambigation models helpstatistical machine translation quality?
model,3-3-P05-1048,ak
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 .
#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
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 .
#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.
tech,15-3-P05-1048,ak
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 .
#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
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 .
#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.
measure(ment),31-3-P05-1048,ak
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 .
#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.
tech,35-3-P05-1048,ak
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 .
#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.
other,16-4-P05-1048,ak
<term>
Error analysis
</term>
suggests several key factors behind this surprising finding , including inherent limitations of current
<term>
statistical MT architectures
</term>
.
#9261Error analysis suggests several key factors behind this surprising finding, including inherent limitations of currentstatistical MT architectures.