Concordance

Query P05-1048 11 >
Shuffle 11 (512.8 per million)

other,10-3-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 .	#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.
other,16-4-P05-1048,bq	<term> Error analysis </term> suggests several key factors behind this surprising finding , including inherent limitations of current <term> statistical MT architectures </term> .	#9404 Error analysis suggests several key factors behind this surprising finding, including inherent limitations of currentstatistical MT architectures.
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 bettertranslation quality than the statistical machine translation system alone.
tech,35-3-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 .	#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,11-1-P05-1048,bq	We directly investigate a subject of much recent debate : do <term> word sense disambigation models </term> help <term> statistical machine translation </term><term> quality </term> ?	#9322 We directly investigate a subject of much recent debate: doword sense disambigation models help statistical machine translation quality?
tool,15-3-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 .	#9362 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),19-1-P05-1048,bq	We directly investigate a subject of much recent debate : do <term> word sense disambigation models </term> help <term> statistical machine translation </term><term> quality </term> ?	#9330 We directly investigate a subject of much recent debate: do word sense disambigation models help statistical machine translationquality?
tech,16-1-P05-1048,bq	We directly investigate a subject of much recent debate : do <term> word sense disambigation models </term> help <term> statistical machine translation </term><term> quality </term> ?	#9327 We directly investigate a subject of much recent debate: do word sense disambigation models helpstatistical machine translation quality?
tech,23-3-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 .	#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.
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.
tech,3-3-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 .	#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.


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