Fundamentals of speech recognition
Fundamentals of speech recognition
A maximum entropy approach to natural language processing
Computational Linguistics
Discriminative training and maximum entropy models for statistical machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
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
Dependency treelet translation: syntactically informed phrasal SMT
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Minimum risk annealing for training log-linear models
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Bagging and Boosting statistical machine translation systems
Artificial Intelligence
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Modern statistical machine translation systems may be seen as using two components: feature extraction, that summarizes information about the translation, and a log-linear framework to combine features. In this paper, we propose to relax the linearity constraints on the combination, and hence relaxing constraints of monotonicity and independence of feature functions. We expand features into a non-parametric, non-linear, and high-dimensional space. We extend empirical Bayes reward training of model parameters to meta parameters of feature generation. In effect, this allows us to trade away some human expert feature design for data. Preliminary results on a standard task show an encouraging improvement.