| model,10-2-I05-2021,ak | designing and evaluating dedicated <term> | word sense disambiguation ( WSD ) models | </term> , in particular with the <term> Senseval | #6382 Much effort has been put in designing and evaluating dedicatedword sense disambiguation ( WSD ) models, in particular with the Senseval series of workshops. | |
| other,34-3-I05-2021,ak | <term> translation </term> of the words in <term> | source language sentences | </term> . Surprisingly however , the <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. | |
| measure(ment),4-4-I05-2021,ak | </term> . Surprisingly however , the <term> | WSD accuracy | </term> of <term> SMT models </term> has never | #6441 Surprisingly however, theWSD accuracy of SMT models has never been evaluated and compared with that of the dedicated WSD models. | |
| model,11-5-I05-2021,ak | accuracy </term> of current typical <term> | SMT models | </term> to be significantly lower than that | #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. | |
| measure(ment),6-5-I05-2021,ak | controlled experiments showing the <term> | WSD accuracy | </term> of current typical <term> SMT models | #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,23-5-I05-2021,ak | lower than that of all the dedicated <term> | WSD models | </term> considered . This tends to support | #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,16-6-I05-2021,ak | speculative claims to the contrary , current <term> | SMT models | </term> do have limitations in comparison | #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. | |
| measure(ment),23-1-I05-2021,ak | Chinese-to-English SMT model </term> directly on <term> | word sense disambiguation performance | </term> , using standard <term> WSD evaluation | #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),10-3-I05-2021,ak | time , the recent improvements in the <term> | BLEU scores | </term> of <term> statistical machine translation | #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. | |
| other,29-3-I05-2021,ak | </term> are good at predicting the right <term> | translation | </term> of the words in <term> source language | #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,37-1-I05-2021,ak | methodology </term> and datasets from the <term> | Senseval-3 Chinese lexical sample task | </term> . Much effort has been put in designing | #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 | improvements in the <term> BLEU scores </term> of <term> | statistical machine translation ( SMT ) | </term> suggests that <term> SMT models </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,30-6-I05-2021,ak | dedicated <term> WSD models </term> , and that <term> | SMT | </term> should benefit from the better predictions | #6517 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,30-1-I05-2021,ak | performance </term> , using standard <term> | WSD evaluation methodology | </term> and datasets from the <term> Senseval-3 | #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. | |
| model,18-1-I05-2021,ak | claim , by evaluating a representative <term> | Chinese-to-English SMT model | </term> directly on <term> word sense disambiguation | #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,7-4-I05-2021,ak | however , the <term> WSD accuracy </term> of <term> | SMT models | </term> has never been evaluated and compared | #6444 Surprisingly however, the WSD accuracy ofSMT models has never been evaluated and compared with that of the dedicated WSD models. | |
| model,40-6-I05-2021,ak | the better predictions made by the <term> | WSD models | </term> . Using <term> natural language processing | #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,25-6-I05-2021,ak | limitations in comparison with dedicated <term> | WSD models | </term> , and that <term> SMT </term> should | #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,20-4-I05-2021,ak | compared with that of the dedicated <term> | WSD models | </term> . We present controlled experiments | #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 | translation ( SMT ) </term> suggests that <term> | SMT models | </term> are good at predicting the right <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. |
