D14-1210 reliable technique for improving monolingual parsing with PCFGs . In this pa - per
D11-1018 alignment ( Section 2.2 ) . The monolingual parsing model is trained to generate
D11-1018 This score is independent of the monolingual parsing model . The fifth line , labeled
D09-1127 straightforward to implement within a monolingual parsing algorithm . In this work we choose
D09-1086 in efficiency and simplicity to monolingual parsing . We showed that augmenting a
D14-1210 grammar refinements comes from the monolingual parsing lit - erature , where phenomena
D11-1018 We evaluate the accuracy of the monolingual parsing models by their span F1 , relative
C02-1003 success in automatic acquisition of monolingual parsing knowledge and grammars . The
D09-1023 learning . 4.1 Translation as Monolingual Parsing We decode by performing lattice
D11-1018 make reordering more robust to monolingual parsing errors , the terminal model is
D11-1018 common words . Distributed or the monolingual parsing model ) and which can be online
D08-1092 syntactic analyses over independent monolingual parsing . We presented a joint log-linear
D09-1127 Klein ( 2008 ) resort to separate monolingual parsing and bilingual reranking over
D08-1023 . This problem also arises in monolingual parsing with probabilistic tree substitution
D11-1018 Monolingual Parsing Model The monolingual parsing model is trained to select the
D09-1127 paradigm , bilingually-constrained monolingual parsing , which is much simpler than
D11-1018 two feature sets directly . 3.2 Monolingual Parsing Features The monolingual parsing
D11-1018 using the CKY algorithm . 2.3 Monolingual Parsing Model The monolingual parsing
D09-1127 to be " 3.8 times slower " than monolingual parsing . Our final results on the test
D11-1018 two halves , A and B , where the monolingual parsing and tree reordering models are
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