| measure(ment),31-3-P05-1048,bq | Using a state-of-the-art <term> Chinese word sense disambiguation model </term> to choose <term> translation candidates </term> for a typical <term> IBM statistical MT system </term> , we find that <term> word sense disambiguation </term> does not yield significantly better <term> translation quality </term> than the <term> statistical machine translation system </term> alone . | #9378 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. |
| tech,0-4-P05-1048,bq | Using a state-of-the-art <term> Chinese word sense disambiguation model </term> to choose <term> translation candidates </term> for a typical <term> IBM statistical MT system </term> , we find that <term> word sense disambiguation </term> does not yield significantly better <term> translation quality </term> than the <term> statistical machine translation system </term> alone . <term> Error analysis </term> suggests several key factors behind this surprising finding , including inherent limitations of current <term> statistical MT architectures </term> . | #9388 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. |
