Ultraconservative online algorithms for multiclass problems
The Journal of Machine Learning Research
Example-Based Machine Translation in the Pangloss system
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
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
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
ORANGE: a method for evaluating automatic evaluation metrics for machine translation
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Online Passive-Aggressive Algorithms
The Journal of Machine Learning Research
Online large-margin training of syntactic and structural translation features
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
The Cunei machine translation platform for WMT '10
WMT '10 Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR
The CMU-EBMT machine translation system
Machine Translation
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Structured perceptrons are attractive due to their simplicity and speed, and have been used successfully for tuning the weights of binary features in a machine translation system. In attempting to apply them to tuning the weights of real-valued features with highly skewed distributions, we found that they did not work well. This paper describes a modification to the update step and compares the performance of the resulting algorithm to standard minimum error-rate training (MERT). In addition, preliminary results for combining MERT or structured-perceptron tuning of the log-linear feature weights with coordinate ascent of other translation system parameters are presented.