Local Gain Adaptation in Stochastic Gradient Descent
Local Gain Adaptation in Stochastic Gradient Descent
BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Statistical phrase-based translation
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Minimum error rate training in statistical machine translation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Hierarchical Phrase-Based Translation
Computational Linguistics
Minimum risk annealing for training log-linear models
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Moses: open source toolkit for statistical machine translation
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
Monte carlo inference and maximization for phrase-based translation
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
Probabilistic inference for machine translation
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Online large-margin training of syntactic and structural translation features
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Decomposability of translation metrics for improved evaluation and efficient algorithms
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Lattice Minimum Bayes-Risk decoding for statistical machine translation
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Fast consensus decoding over translation forests
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
Variational decoding for statistical machine translation
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Consensus training for consensus decoding in machine translation
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
SampleRank training for phrase-based machine translation
WMT '11 Proceedings of the Sixth Workshop on Statistical Machine Translation
Minimum bayes risk decoding with enlarged hypothesis space in system combination
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part II
Hope and fear for discriminative training of statistical translation models
The Journal of Machine Learning Research
Batch tuning strategies for statistical machine translation
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
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We present a unified approach to performing minimum risk training and minimum Bayes risk (MBR) decoding with BLEU in a phrase-based model. Key to our approach is the use of a Gibbs sampler that allows us to explore the entire probability distribution and maintain a strict probabilistic formulation across the pipeline. We also describe a new sampling algorithm called corpus sampling which allows us at training time to use BLEU instead of an approximation thereof. Our approach is theoretically sound and gives better (up to +0.6%BLEU) and more stable results than the standard MERT optimization algorithm. By comparing our approach to lattice MBR, we are also able to gain crucial insights about both methods.