C04-1045 during the EM training of the statistical alignment models . The evaluation is done
C04-1005 + + toolkit is used to perform statistical alignment . Thus , for each sentence pair
C04-1045 POS information for improving statistical alignment quality of the HMM-based model
C04-1045 dependencies into the training of the statistical alignment models . Existing statistical
E06-1005 systems is used for the unsupervised statistical alignment training . Thus , the decision
C02-1009 alignment - candidate . Then the statistical alignment model is to find the Bayesian
E06-1005 of confusion net - works . 2.1 Statistical Alignment The word alignment is performed
E06-1005 translation hypotheses with an enhanced statistical alignment algorithm that explicitly models
C04-1045 to improve the quality of the statistical alignments by taking into account the interdependencies
J03-1002 word alignment . So far , refined statistical alignment models have in general been rarely
E14-2013 bilingual dictionaries or by learning statistical alignment models out of bilingual corpora
D15-1051 2012 ) , in order to improve the statistical alignment models and make them more expressive
J03-1002 various design decisions of our statistical alignment system and evaluate these on
J03-1002 perform a symmetrization of directed statistical alignment models . As evaluation criterion
E03-1007 POS information for improving statistical alignment quality is described in ( Toutanova
J03-1002 Systematic Comparison of Various Statistical Alignment Models </title> Josef Hermann
C00-2163 Conclusion We have evaluated vm ` ious statistical alignment models by conlparing the Viterbi
E09-1020 one-size-fits-all " approach generally used in Statistical alignment and translation . Several interesting
E12-1010 ) for a comparison of various statistical alignment models . In our case however
C04-1006 lexicon symmetrization methods for statistical alignment models that are trained using
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