| 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. |
| 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. |
| 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. |
| 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. |
| 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,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,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. |
| 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. |
| 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,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. |
| 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. |
| 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. |
| 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),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),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. |
| tech,25-6-I05-2021,bq | limitations in comparison with dedicated <term> | WSD models | </term> , and that <term> SMT </term> should | #7969 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. |
| tech,40-6-I05-2021,bq | the better predictions made by the <term> | WSD models | </term> . <term> Statistical machine translation | #7984 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. |
| 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. |
| 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. |
| 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. |
