W03-0303 was shown to be effective in our Chinese-English alignment experiments . The first two passes
P09-1104 the best AER on the NIST 2002 Chinese-English alignment data set . Furthermore , they
W03-0303 French-English and Romanian-English . For Chinese-English alignment , 365 sentence-pairs are randomly
P06-2112 distortion probability for head word in Chinese-English alignment . PrEJ ( Δj | Δj '
P09-1104 2005 ) . 6.2 Chinese NIST Results Chinese-English alignment is a much harder task than French-English
D10-1090 . Subse - quently , word level Chinese-English alignments are generated using the Berkeley
P11-1133 we reached a 77 % F-Measure for Chinese-English alignment using a classifier trained on
P09-1104 the lowest published error for Chinese-English alignment and an increase in downstream
D15-1210 Setup We evaluate our approach on Chinese-English alignment and translation tasks . The training
P13-1017 . We use the manually aligned Chinese-English alignment corpus ( Haghighi et al. , 2009
W03-0303 alignment tasks , and in addition to Chinese-English alignment . For Chinese - English and French-English
W02-1802 present some significant issues for Chinese-English alignment and term extraction for the construction
W05-1205 ITGs produces significantly lower Chinese-English alignment error rates than a syntactically
W03-0303 first describe our experiments on Chinese-English alignment , and then the results for the
P09-1105 MaxEnt aligner , we reduced the Chinese-English alignment error by 5 % and the Arabic-English
D11-1082 different starting aligners . 6.2 Chinese-English alignment results Table ( 2 ) presents
N12-1078 examined 1/10 of all 5000 sampled Chinese-English alignments at random and found only 3 of
P09-1104 provide a much closer fit to true Chinese-English alignments than A1-1 . 3 Margin-Based Training
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