| 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,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. |
| 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,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,4-4-I05-2021,bq | </term> . Surprisingly however , the <term> | WSD | </term><term> accuracy </term> of <term> SMT | #7898 Surprisingly however, theWSD accuracy of SMT models has never been evaluated and compared with that of the dedicated 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. |
