Concordance

Query I05-2021 21 >
Multilevel Sort 21 (672.3 per million)

measure(ment),10-3-I05-2021,ak	At the same time , the recent improvements in the <term> BLEU scores </term> of <term> statistical machine translation ( SMT ) </term> suggests that <term> SMT models </term> are good at predicting the right <term> translation </term> of the words in <term> source language sentences </term> .	#6409 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,ak	We present the first known empirical test of an increasingly common speculative claim , by evaluating a representative <term> Chinese-to-English SMT model </term> directly on <term> word sense disambiguation performance </term> , using standard <term> WSD evaluation methodology </term> and datasets from the <term> Senseval-3 Chinese lexical sample task </term> .	#6352 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),4-4-I05-2021,ak	Surprisingly however , the <term> WSD accuracy </term> of <term> SMT models </term> has never been evaluated and compared with that of the dedicated <term> WSD models </term> .	#6441 Surprisingly however, theWSD accuracy of SMT models has never been evaluated and compared with that of the dedicated WSD models.
measure(ment),6-5-I05-2021,ak	We present controlled experiments showing the <term> WSD accuracy </term> of current typical <term> SMT models </term> to be significantly lower than that of all the dedicated <term> WSD models </term> considered .	#6466 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.
model,10-2-I05-2021,ak	Much effort has been put in designing and evaluating dedicated <term> word sense disambiguation ( WSD ) models </term> , in particular with the <term> Senseval series of workshops </term> .	#6382 Much effort has been put in designing and evaluating dedicatedword sense disambiguation ( WSD ) models, in particular with the Senseval series of workshops.
model,11-5-I05-2021,ak	We present controlled experiments showing the <term> WSD accuracy </term> of current typical <term> SMT models </term> to be significantly lower than that of all the dedicated <term> WSD models </term> considered .	#6471 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.
model,16-6-I05-2021,ak	This tends to support the view that despite recent speculative claims to the contrary , current <term> SMT models </term> do have limitations in comparison with dedicated <term> WSD models </term> , and that <term> SMT </term> should benefit from the better predictions made by the <term> WSD models </term> .	#6503 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.
model,18-1-I05-2021,ak	We present the first known empirical test of an increasingly common speculative claim , by evaluating a representative <term> Chinese-to-English SMT model </term> directly on <term> word sense disambiguation performance </term> , using standard <term> WSD evaluation methodology </term> and datasets from the <term> Senseval-3 Chinese lexical sample task </term> .	#6347 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.
model,20-4-I05-2021,ak	Surprisingly however , the <term> WSD accuracy </term> of <term> SMT models </term> has never been evaluated and compared with that of the dedicated <term> WSD models </term> .	#6457 Surprisingly however, the WSD accuracy of SMT models has never been evaluated and compared with that of the dedicatedWSD models.
model,21-3-I05-2021,ak	At the same time , the recent improvements in the <term> BLEU scores </term> of <term> statistical machine translation ( SMT ) </term> suggests that <term> SMT models </term> are good at predicting the right <term> translation </term> of the words in <term> source language sentences </term> .	#6420 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.
model,23-5-I05-2021,ak	We present controlled experiments showing the <term> WSD accuracy </term> of current typical <term> SMT models </term> to be significantly lower than that of all the dedicated <term> WSD models </term> considered .	#6483 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.
model,25-6-I05-2021,ak	This tends to support the view that despite recent speculative claims to the contrary , current <term> SMT models </term> do have limitations in comparison with dedicated <term> WSD models </term> , and that <term> SMT </term> should benefit from the better predictions made by the <term> WSD models </term> .	#6512 This tends to support the view that despite recent speculative claims to the contrary, current SMT models do have limitations in comparison with dedicatedWSD models, and that SMT should benefit from the better predictions made by the WSD models.
model,40-6-I05-2021,ak	This tends to support the view that despite recent speculative claims to the contrary , current <term> SMT models </term> do have limitations in comparison with dedicated <term> WSD models </term> , and that <term> SMT </term> should benefit from the better predictions made by the <term> WSD models </term> .	#6527 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 that SMT should benefit from the better predictions made by theWSD models.
model,7-4-I05-2021,ak	Surprisingly however , the <term> WSD accuracy </term> of <term> SMT models </term> has never been evaluated and compared with that of the dedicated <term> WSD models </term> .	#6444 Surprisingly however, the WSD accuracy ofSMT models has never been evaluated and compared with that of the dedicated WSD models.
other,22-2-I05-2021,ak	Much effort has been put in designing and evaluating dedicated <term> word sense disambiguation ( WSD ) models </term> , in particular with the <term> Senseval series of workshops </term> .	#6394 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,ak	At the same time , the recent improvements in the <term> BLEU scores </term> of <term> statistical machine translation ( SMT ) </term> suggests that <term> SMT models </term> are good at predicting the right <term> translation </term> of the words in <term> source language sentences </term> .	#6428 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,34-3-I05-2021,ak	At the same time , the recent improvements in the <term> BLEU scores </term> of <term> statistical machine translation ( SMT ) </term> suggests that <term> SMT models </term> are good at predicting the right <term> translation </term> of the words in <term> source language sentences </term> .	#6433 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,ak	We present the first known empirical test of an increasingly common speculative claim , by evaluating a representative <term> Chinese-to-English SMT model </term> directly on <term> word sense disambiguation performance </term> , using standard <term> WSD evaluation methodology </term> and datasets from the <term> Senseval-3 Chinese lexical sample task </term> .	#6366 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,13-3-I05-2021,ak	At the same time , the recent improvements in the <term> BLEU scores </term> of <term> statistical machine translation ( SMT ) </term> suggests that <term> SMT models </term> are good at predicting the right <term> translation </term> of the words in <term> source language sentences </term> .	#6412 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,30-1-I05-2021,ak	We present the first known empirical test of an increasingly common speculative claim , by evaluating a representative <term> Chinese-to-English SMT model </term> directly on <term> word sense disambiguation performance </term> , using standard <term> WSD evaluation methodology </term> and datasets from the <term> Senseval-3 Chinese lexical sample task </term> .	#6359 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.


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