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: do word 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 help statistical machine translation quality? |
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 translation quality ? |
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 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. |
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 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,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 that word sense disambiguation does not yield significantly better translation quality than the statistical machine translation system alone. |
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 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 the statistical machine translation system alone. |
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. |
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 current statistical MT architectures. |