[PDF] Top 20 Regularized Minimum Error Rate Training
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Regularized Minimum Error Rate Training
... Secondly, it offers a globally optimal line search. Un- fortunately, there are several potential difficulties in scaling MERT to larger numbers of features, due to its non-convex loss function and its lack of reg- ... See full document
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Adaptive near minimum error rate training for neural networks with application to multiuser detection in CDMA communication systems
... MER training algorithm for a class of neural networkclassifiers that includes nonlinear equalizers and multiuser detec- ...MER training can in theory be achieved by only adjusting the classifier parameters ... See full document
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Adaptive Minimum Bit Error Rate Beamforming
... Fig. 7. Learning curves of the stochastic gradient adaptive MBER algorithm of Section IV-B averaged over 20 randomly chosen uniformly distributed initial weight values w(0) . SNR = 10 dB, SIR = SIR = 06 dB, and SIR = SIR ... See full document
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Adaptive Least Error Rate Algorithm for Neural Network Classifiers
... We propose a stochastic gradient adaptive minimum error rate MER algorithm called the least error rate LER for training neural network classi ers.. INTRODUCTION We study the class of neu[r] ... See full document
10
Adaptive minimum bit error rate filtering
... averaged over 30 runs, given SNR ¼ 12 dB and two different initial weight vector conditions. From Fig. 4, it can be seen that this stochastic gradient adaptive MBER algorithm has a reasonable convergence rate. ... See full document
10
Modeling Letter to Phoneme Conversion as a Phrase Based Statistical Machine Translation Problem with Minimum Error Rate Training
... Intuitively, the performance of the system can be improved in at least two areas. First is the Minimum Error Rate Training (MERT) and the second is the decoding phase. Using phonetic feature ... See full document
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Distributed Minimum Error Rate Training of SMT using Particle Swarm Optimization
... metric such as BLEU-score (Papineni et al., 2002). In fact, minimum error rate training (MERT) pro- posed by (Och, 2003) is the most widely used pa- rameter tuning method in SMT community. ... See full document
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Efficient Minimum Error Rate Training and Minimum Bayes Risk Decoding for Translation Hypergraphs and Lattices
... We observed in the NIST systems that MERT resulted in short translations relative to MAP on the unseen test set. To prevent this behavior, we modify the MERT error criterion to include a sentence-level brevity ... See full document
9
Parsing-based Machine Translation using an Open Source Toolkit: Joshua for Tamil Language
... Abstract- Joshua, an open source toolkit for statistical machine translation. It implements all of the algorithms required for synchronous context free grammars (SCFGs): chart-parsing, n-gram language model integration, ... See full document
5
Lattice Based Minimum Error Rate Training Using Weighted Finite State Transducers with Tropical Polynomial Weights
... We now wish to find the hypotheses that define the error surface by performing Steps 5 and 6 of the TGMERT algorithm. These two linear functions de- fine two intervals of γ . The linear functions intersect at γ ≈ ... See full document
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A Systematic Comparison of Training Criteria for Statistical Machine Translation
... A problem that we often face in practice is that the correct translation might not be among the can- didates that our MT system produces. Therefore, (Och and Ney, 2002; Och, 2003) defined the trans- lation candidate with ... See full document
9
Locally Training the Log Linear Model for SMT
... translation, minimum error rate training (MERT) is a standard method for tuning a single weight with regard to a given development ...local training method to address these two ... See full document
10
The Geometry of Statistical Machine Translation
... is minimum error rate training (MERT), which is an efficient form of line optimisation adapted to the highly non- linear objective functions used in machine ...the error surface over a ... See full document
11
Minimum Sample Risk Methods for Language Modeling
... called minimum sample risk ...discriminative training methods in that instead of searching on an ap- proximated loss function, MSR employs a simple heuristic training algorithm that minimizes the ... See full document
8
Joint Decoding with Multiple Translation Models
... Table 2 shows the results of comparing individ- ual decoding and joint decoding on the test set. With conventional max-derivation decoding, the hierarchical phrase-based model achieved a BLEU score of 30.11 on the test ... See full document
9
Adaptive Minimum Bit Error Rate Beamforming Assisted Receiver for Wireless Communications
... MMSE decision directed training MBER.. Bit Error Rate.[r] ... See full document
5
Smart Beamforming for Wireless Communications: A Novel Minimum Bit Error Rate Approach
... 1e-4 MMSE decision directed training MBER.. MMSE decision directed training MBER.[r] ... See full document
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On the Rate of Convergence of Regularized Boosting Classifiers
... A regularized boosting method is introduced, for which regularization is obtained through a pe- nalization ...The rate of convergence of the probability of misclassification is ...of error converges ... See full document
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Random Restarts in Minimum Error Rate Training for Statistical Machine Translation
... introduced minimum error rate train- ing (MERT) for optimizing feature weights in sta- tistical machine translation (SMT) models, and demonstrated that it produced higher translation quality scores ... See full document
8
Transductive Minimum Error Rate Training for Statistical Machine Translation
... We can also review the roles that the develop- ment and test datasets play in the procedure of avoiding over-training. The reason for that we transductively generate translations as pseudo ref- erences for test ... See full document
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