| tech,11-1-P05-1048,bq | subject of much recent debate : do <term> | word sense disambigation models | </term> help <term> statistical machine translation | #9322 We directly investigate a subject of much recent debate: doword sense disambigation models help statistical machine translation quality? |
| tech,16-1-P05-1048,bq | sense disambigation models </term> help <term> | statistical machine translation | </term><term> quality </term> ? We present | #9327 We directly investigate a subject of much recent debate: do word sense disambigation models helpstatistical machine translation quality? |
| measure(ment),19-1-P05-1048,bq | statistical machine translation </term><term> | quality | </term> ? We present empirical results casting | #9330 We directly investigate a subject of much recent debate: do word sense disambigation models help statistical machine translationquality? |
| tech,3-3-P05-1048,bq | assumption . Using a state-of-the-art <term> | Chinese word sense disambiguation model | </term> to choose <term> translation candidates | #9350 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. |
| other,10-3-P05-1048,bq | disambiguation model </term> to choose <term> | translation candidates | </term> for a typical <term> IBM statistical | #9357 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. |
| tech,23-3-P05-1048,bq | statistical MT system </term> , we find that <term> | word sense disambiguation | </term> does not yield significantly better | #9370 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. |
| measure(ment),31-3-P05-1048,bq | does not yield significantly better <term> | translation quality | </term> than the <term> statistical machine | #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 bettertranslation quality than the statistical machine translation system alone. |
| tech,35-3-P05-1048,bq | translation quality </term> than the <term> | statistical machine translation system | </term> alone . <term> Error analysis </term> | #9382 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. |
| tech,0-4-P05-1048,bq | machine translation system </term> alone . <term> | Error analysis | </term> suggests several key factors behind | #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. |
| other,16-4-P05-1048,bq | including inherent limitations of current <term> | statistical MT architectures | </term> . <term> Syntax-based statistical machine | #9404 Error analysis suggests several key factors behind this surprising finding, including inherent limitations of currentstatistical MT architectures. |
