D14-1026 correlation with human judgment by hill climbing with 100 random restarts using
E09-1059 non-trivial we use an approximate hill climbing technique . First we randomly
D14-1217 results we present . We use a simple hill climbing strategy to find a reasonable
D11-1103 lattices in the second pass . A hill climbing method ( iterative decoding )
E06-2012 Alembic name tagger by manual hill climbing . Because this tagger was originally
H93-1021 likelihood guarantees that any hill climbing method will converge to the global
D12-1108 test sets demonstrates that the hill climbing decoder manages to fix some of
H93-1020 Since the training is an iterative hill climbing tech - nique , initialization
D12-1108 State Initialisation Before the hill climbing decoding algorithm can be run
D12-1108 Our decoder 's first - choice hill climbing strategy never enumerates the
H92-1036 estimate of ~ o , we obtain a hill climbing procedure by alternate maximization
D12-1108 row were obtained by running the hill climbing decoder with DP initialisation
D09-1161 points ( the Markov link ) for hill climbing . However , it accepts some bad
D08-1095 based on an error backpropagation hill climbing algorithm ( Diligenti et al.
C02-1020 correspondence level of the word pairs by hill climbing . These methods could archive
E12-1073 which can be optimized using e.g. hill climbing . TERp being a tunable metric
D11-1103 are formed , we follow a similar hill climbing procedure as proposed in our
D09-1161 temperature ( line 7 - 10 ) . The hill climbing nature gives this algorithm the
D12-1108 language model . 2 SMT Decoding by Hill Climbing In this section , we formally
D12-1108 Search Algorithm Parameters The hill climbing algorithm we use has two parameters
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