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),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. |
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. |
measure(ment),30-1-I05-2021,bq |
performance
</term>
, using standard
<term>
|
WSD evaluation methodology
|
</term>
and
<term>
datasets
</term>
from the
<term>
|
#7816
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 standardWSD evaluation methodology and datasets 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. |
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. |
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. |
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,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. |
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. |
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. |
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,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. |
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. |
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. |
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,20-4-I05-2021,bq |
compared with that of the dedicated
<term>
|
WSD models
|
</term>
. We present controlled experiments
|
#7914
Surprisingly however, the WSD accuracy of SMT models has never been evaluated and compared with that of the dedicatedWSD 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,23-5-I05-2021,bq |
lower than that of all the dedicated
<term>
|
WSD models
|
</term>
considered . This tends to support
|
#7940
We present controlled experiments showing the WSD accuracy of current typical SMT models to be significantly lower than that of all the dedicatedWSD models considered. |