From cdec Decoder
cdec supports multi-pass scoring of a translation forest. That is, a large search space can be rescored with coarse, but inexpensive features, to determine the least promising portions, which can then be pruned. Then progressively more fine-grained (and expensive-to-compute) features can be applied on the pruned space.
formalism=scfg add_pass_through_rules=true # first pass features -- LM is a bigram LM feature_function=NonLatinCount feature_function=WordPenalty feature_function=KLanguageModel /fs/clip-dissertation/twophase/a2e.2gm.klm #first pass weights weights=/fs/clip-dissertation/twophase/second-pass-mert/weights.first #the amount of the search space to prune density_prune=240 # second pass features feature_function2=LanguageModel lm://dsub01.umiacs.umd.edu:6668 -o 5 -n LM2
Using this functionality, cdec could implement "Coarse-to-Fine Syntactic Machine Translation using Language Projections" Slav Petrov, Aria Haghighi and Dan Klein, EMNLP 2008