Recent work on training of log-linear interpolation mod- els for statistical machine translation reported perfor- mance improvements by optimizing parameters with re- spect to translation quality, rather than to likelihood ori- ented criteria. This work presents an alternative and more direct training procedure for log-linear interpola- tion models. In addition, we point out the subtle inter- action between log-linear models and the beam search algorithm. Experimental results are reported on two Chinese-English evaluation sets, C-Star 2003 and Nist 2003, by using a statistical phrase-based model derived from Model 4. By optimizing parameters with respect to the BLUE score, performance relative improvements by 9.6% and 2.8% were achieved, respectively
Minimum Error Training of Log-Linear Translation Models
Cettolo, Mauro;Federico, Marcello
2004-01-01
Abstract
Recent work on training of log-linear interpolation mod- els for statistical machine translation reported perfor- mance improvements by optimizing parameters with re- spect to translation quality, rather than to likelihood ori- ented criteria. This work presents an alternative and more direct training procedure for log-linear interpola- tion models. In addition, we point out the subtle inter- action between log-linear models and the beam search algorithm. Experimental results are reported on two Chinese-English evaluation sets, C-Star 2003 and Nist 2003, by using a statistical phrase-based model derived from Model 4. By optimizing parameters with respect to the BLUE score, performance relative improvements by 9.6% and 2.8% were achieved, respectivelyI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
