H92-1035 achieved successfully via the N-best rescoring paradigm . The N-best lists are
D15-1121 inference is to use them in an N-best rescoring step . In this paper , we focus
D14-1013 cascade approach with bi-directional n-best rescoring . In Section 6 , we compare the
D08-1086 of recognition candidates by an n-best rescoring . Unlike other methods , this
H92-1093 optima in weight estimation for N-Best rescoring . However we find that there
H92-1035 We call this whole process the N-best rescoring paradigm . The linear combination
H92-1035 CSR system . We have used the N-best rescoring paradigm to achieve this improvement
D15-1121 This proposal is evaluated in an N-best rescoring step , using the framework of
H93-1015 a result we are modifying the N-best rescoring to alleviate this problem . 5
D15-1139 neural network language model N-best rescoring in de - coding . Both systems
H92-1093 . In our implementation of the N-Best rescoring paradigm \ -LSB- 1 \ -RSB- ,
D15-1165 decoder has a slight advantage over n-best rescoring . Therefore , we apply RNNs in
H92-1100 for HMM and SSM scores in the N-best rescoring paradigm . Addressed the problem
D13-1053 5-gram language model used in n-best rescoring . The systems are tuned and evaluated
E14-1003 over a trigram MTU model in an n-best rescoring setting . Our experiments have
D15-1121 CTM into the SMT system through N-best rescoring . A major difference with most
H92-1035 performance of the SNN in the N-best rescoring paradigm . If , for example ,
D13-1106 have previously only been used in n-best rescoring settings and on small-scale tasks
H92-1035 improvement in two ways . Firstly , the N-best rescoring paradigm has allowed us to design
E14-1003 MTU RNN model ( § 4 ) in an n-best rescoring setting , comparing against a
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