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 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),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 translation quality ? |
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 current statistical MT architectures. |
tech,35-3-P05-1048,bq |
translation quality
</term>
than the
<term>
|
statistical
machine translation system
|
</term>
alone .
<term>
Error analysis
</term>
|
#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,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 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 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,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: do word 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 help statistical machine translation quality? |
tech,0-4-P05-1048,bq |
translation system
</term>
alone .
<term>
|
Error
analysis
|
</term>
suggests several key factors behind
|
#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. |
measure(ment),31-3-P05-1048,bq |
does not yield significantly better
<term>
|
translation
quality
|
</term>
than the
<term>
statistical machine
|
#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. |