| model,3-3-P05-1048,ak | assumption . Using a state-of-the-art <term> | Chinese word sense disambiguation model | </term> to choose <term> translation candidates | #9207 Using a state-of-the-artChinese word sense disambiguation model to choose translation candidates for a typical IBM statistical MT system, we find that word sense disambiguation does not yield significantly better translation quality than the statistical machine translation system alone. | |
| tech,0-4-P05-1048,ak | machine translation system </term> alone . <term> | Error analysis | </term> suggests several key factors behind | #9245 Using a state-of-the-art Chinese word sense disambiguation model to choose translation candidates for a typical IBM statistical MT system, we find that word sense disambiguation does not yield significantly better translation quality than the statistical machine translation system alone.Error analysis suggests several key factors behind this surprising finding, including inherent limitations of current statistical MT architectures. | |
| tech,15-3-P05-1048,ak | translation candidates </term> for a typical <term> | IBM statistical MT system | </term> , we find that <term> word sense disambiguation | #9219 Using a state-of-the-art Chinese word sense disambiguation model to choose translation candidates for a typicalIBM statistical MT system, we find that word sense disambiguation does not yield significantly better translation quality than the statistical machine translation system alone. | |
| measure(ment),16-1-P05-1048,ak | sense disambigation models </term> help <term> | statistical machine translation quality | </term> ? We present empirical results casting | #9184 We directly investigate a subject of much recent debate: do word sense disambigation models helpstatistical machine translation quality? | |
| tech,35-3-P05-1048,ak | translation quality </term> than the <term> | statistical machine translation system | </term> alone . <term> Error analysis </term> | #9239 Using a state-of-the-art Chinese word sense disambiguation model to choose translation candidates for a typical IBM statistical MT system, we find that word sense disambiguation does not yield significantly better translation quality than thestatistical machine translation system alone. | |
| other,16-4-P05-1048,ak | including inherent limitations of current <term> | statistical MT architectures | </term> . Extracting <term> semantic relationships | #9261 Error analysis suggests several key factors behind this surprising finding, including inherent limitations of currentstatistical MT architectures. | |
| other,10-3-P05-1048,ak | disambiguation model </term> to choose <term> | translation candidates | </term> for a typical <term> IBM statistical | #9214 Using a state-of-the-art Chinese word sense disambiguation model to choosetranslation candidates for a typical IBM statistical MT system, we find that word sense disambiguation does not yield significantly better translation quality than the statistical machine translation system alone. | |
| measure(ment),31-3-P05-1048,ak | does not yield significantly better <term> | translation quality | </term> than the <term> statistical machine | #9235 Using a state-of-the-art Chinese word sense disambiguation model to choose translation candidates for a typical IBM statistical MT system, we find that word sense disambiguation does not yield significantly bettertranslation quality than the statistical machine translation system alone. | |
| model,11-1-P05-1048,ak | subject of much recent debate : do <term> | word sense disambigation models | </term> help <term> statistical machine translation | #9179 We directly investigate a subject of much recent debate: doword sense disambigation models help statistical machine translation quality? | |
| tech,23-3-P05-1048,ak | statistical MT system </term> , we find that <term> | word sense disambiguation | </term> does not yield significantly better | #9227 Using a state-of-the-art Chinese word sense disambiguation model to choose translation candidates for a typical IBM statistical MT system, we find thatword sense disambiguation does not yield significantly better translation quality than the statistical machine translation system alone. |
