measure(ment),5-4-I05-2021,bq |
Surprisingly however , the
<term>
WSD
</term><term>
|
accuracy
|
</term>
of
<term>
SMT models
</term>
has never
|
#7899
Surprisingly however, the WSDaccuracy of SMT models has never been evaluated and compared with that of the dedicated WSD models. |
measure(ment),7-5-I05-2021,bq |
experiments showing the
<term>
WSD
</term><term>
|
accuracy
|
</term>
of current typical
<term>
SMT models
|
#7924
We present controlled experiments showing the WSDaccuracy of current typical SMT models to be significantly lower than that of all the dedicated WSD models considered. |
measure(ment),10-3-I05-2021,bq |
time , the recent improvements in the
<term>
|
BLEU scores
|
</term>
of
<term>
statistical machine translation
|
#7866
At the same time, the recent improvements in theBLEU scores of statistical machine translation (SMT) suggests that SMT models are good at predicting the right translation of the words in source language sentences. |
model,18-1-I05-2021,bq |
claim , by evaluating a representative
<term>
|
Chinese-to-English SMT model
|
</term>
directly on
<term>
word sense disambiguation
|
#7804
We present the first known empirical test of an increasingly common speculative claim, by evaluating a representativeChinese-to-English SMT model directly on word sense disambiguation performance, using standard WSD evaluation methodology and datasets from the Senseval-3 Chinese lexical sample task. |
lr,34-1-I05-2021,bq |
WSD evaluation methodology
</term>
and
<term>
|
datasets
|
</term>
from the
<term>
Senseval-3 Chinese
|
#7820
We present the first known empirical test of an increasingly common speculative claim, by evaluating a representative Chinese-to-English SMT model directly on word sense disambiguation performance, using standard WSD evaluation methodology anddatasets from the Senseval-3 Chinese lexical sample task. |
measure(ment),5-1-I05-2021,bq |
</term>
. We present the first known
<term>
|
empirical test
|
</term>
of an increasingly common speculative
|
#7791
We present the first knownempirical test of an increasingly common speculative claim, by evaluating a representative Chinese-to-English SMT model directly on word sense disambiguation performance, using standard WSD evaluation methodology and datasets from the Senseval-3 Chinese lexical sample task. |
other,22-2-I05-2021,bq |
models
</term>
, in particular with the
<term>
|
Senseval
|
</term>
series of workshops . At the same
|
#7851
Much effort has been put in designing and evaluating dedicated word sense disambiguation (WSD) models, in particular with theSenseval series of workshops. |
other,37-1-I05-2021,bq |
</term>
and
<term>
datasets
</term>
from the
<term>
|
Senseval-3 Chinese lexical sample task
|
</term>
. Much effort has been put in designing
|
#7823
We present the first known empirical test of an increasingly common speculative claim, by evaluating a representative Chinese-to-English SMT model directly on word sense disambiguation performance, using standard WSD evaluation methodology and datasets from theSenseval-3 Chinese lexical sample task. |
tech,30-6-I05-2021,bq |
dedicated
<term>
WSD models
</term>
, and that
<term>
|
SMT
|
</term>
should benefit from the better predictions
|
#7974
This tends to support the view that despite recent speculative claims to the contrary, current SMT models do have limitations in comparison with dedicated WSD models, and thatSMT should benefit from the better predictions made by the WSD models. |
tech,21-3-I05-2021,bq |
translation ( SMT )
</term>
suggests that
<term>
|
SMT models
|
</term>
are good at predicting the right
<term>
|
#7877
At the same time, the recent improvements in the BLEU scores of statistical machine translation (SMT) suggests thatSMT models are good at predicting the right translation of the words in source language sentences. |
tech,7-4-I05-2021,bq |
<term>
WSD
</term><term>
accuracy
</term>
of
<term>
|
SMT models
|
</term>
has never been evaluated and compared
|
#7901
Surprisingly however, the WSD accuracy ofSMT models has never been evaluated and compared with that of the dedicated WSD models. |
tech,16-6-I05-2021,bq |
speculative claims to the contrary , current
<term>
|
SMT models
|
</term>
do have limitations in comparison
|
#7960
This tends to support the view that despite recent speculative claims to the contrary, currentSMT models do have limitations in comparison with dedicated WSD models, and that SMT should benefit from the better predictions made by the WSD models. |
tech,11-5-I05-2021,bq |
<term>
accuracy
</term>
of current typical
<term>
|
SMT models
|
</term>
to be significantly lower than that
|
#7928
We present controlled experiments showing the WSD accuracy of current typicalSMT models to be significantly lower than that of all the dedicated WSD models considered. |
other,34-3-I05-2021,bq |
translation
</term>
of the
<term>
words
</term>
in
<term>
|
source language sentences
|
</term>
. Surprisingly however , the
<term>
|
#7890
At the same time, the recent improvements in the BLEU scores of statistical machine translation (SMT) suggests that SMT models are good at predicting the right translation of the words insource language sentences. |
tech,13-3-I05-2021,bq |
improvements in the
<term>
BLEU scores
</term>
of
<term>
|
statistical machine translation ( SMT )
|
</term>
suggests that
<term>
SMT models
</term>
|
#7869
At the same time, the recent improvements in the BLEU scores ofstatistical machine translation ( SMT ) suggests that SMT models are good at predicting the right translation of the words in source language sentences. |
other,29-3-I05-2021,bq |
</term>
are good at predicting the right
<term>
|
translation
|
</term>
of the
<term>
words
</term>
in
<term>
source
|
#7885
At the same time, the recent improvements in the BLEU scores of statistical machine translation (SMT) suggests that SMT models are good at predicting the righttranslation of the words in source language sentences. |
tech,10-2-I05-2021,bq |
designing and evaluating dedicated
<term>
|
word sense disambiguation ( WSD ) models
|
</term>
, in particular with the
<term>
Senseval
|
#7839
Much effort has been put in designing and evaluating dedicatedword sense disambiguation ( WSD ) models, in particular with the Senseval series of workshops. |
measure(ment),23-1-I05-2021,bq |
Chinese-to-English SMT model
</term>
directly on
<term>
|
word sense disambiguation performance
|
</term>
, using standard
<term>
WSD evaluation
|
#7809
We present the first known empirical test of an increasingly common speculative claim, by evaluating a representative Chinese-to-English SMT model directly onword sense disambiguation performance, using standard WSD evaluation methodology and datasets from the Senseval-3 Chinese lexical sample task. |
other,32-3-I05-2021,bq |
right
<term>
translation
</term>
of the
<term>
|
words
|
</term>
in
<term>
source language sentences
|
#7888
At the same time, the recent improvements in the BLEU scores of statistical machine translation (SMT) suggests that SMT models are good at predicting the right translation of thewords in source language sentences. |
tech,6-5-I05-2021,bq |
controlled experiments showing the
<term>
|
WSD
|
</term><term>
accuracy
</term>
of current typical
|
#7923
We present controlled experiments showing theWSD accuracy of current typical SMT models to be significantly lower than that of all the dedicated WSD models considered. |