BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Minimum error rate training in statistical machine translation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
An end-to-end discriminative approach to machine translation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Online Passive-Aggressive Algorithms
The Journal of Machine Learning Research
Minimum risk annealing for training log-linear models
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Learning and inference in weighted logic with application to natural language processing
Learning and inference in weighted logic with application to natural language processing
Monte carlo inference and maximization for phrase-based translation
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
Online large-margin training of syntactic and structural translation features
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Experiments in domain adaptation for statistical machine translation
StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation
Stabilizing minimum error rate training
StatMT '09 Proceedings of the Fourth Workshop on Statistical Machine Translation
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Distributed training strategies for the structured perceptron
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
A unified approach to minimum risk training and decoding
WMT '10 Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR
Optimized online rank learning for 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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Statistical machine translation systems are normally optimised for a chosen gain function (metric) by using MERT to find the best model weights. This algorithm suffers from stability problems and cannot scale beyond 20-30 features. We present an alternative algorithm for discriminative training of phrase-based MT systems, SampleRank, which scales to hundreds of features, equals or beats MERT on both small and medium sized systems, and permits the use of sentence or document level features. SampleRank proceeds by repeatedly updating the model weights to ensure that the ranking of output sentences induced by the model is the same as that induced by the gain function.