A Cache-Based Natural Language Model for Speech Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Phrase-Based Statistical Machine Translation
KI '02 Proceedings of the 25th Annual German Conference on AI: Advances in Artificial Intelligence
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
Discriminative training and maximum entropy models for statistical 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
TransType2: an innovative computer-assisted translation system
ACLdemo '04 Proceedings of the ACL 2004 on Interactive poster and demonstration sessions
Language model adaptation for statistical machine translation with structured query models
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
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
Domain adaptation in statistical machine translation with mixture modelling
StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation
Experiments in domain adaptation for statistical machine translation
StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation
Domain adaptation for statistical machine translation with monolingual resources
StatMT '09 Proceedings of the Fourth Workshop on Statistical Machine Translation
CEU-UPV English-Spanish system for WMT11
WMT '11 Proceedings of the Sixth Workshop on Statistical Machine Translation
Encouraging consistent translation choices
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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In many pattern recognition problems, learning from training samples is a process that requires important amounts of training data and a high computational effort. Sometimes, only limited training data and/or limited computational resources are available, but there is also available a previous system trained for a closely related task and with enough training material. This scenario is very frequent in statistical machine translation and adaptation can be a solution to deal with this problem. In this paper, we present an adaptation technique for (state-of-the-art) log-linear modelling based on the well-known Bayesian learning paradigm. This technique has been applied to statistical machine translation and can be easily extended to other pattern recognition areas in which log-linear models are used. We show empirical results in which a small amount of adaptation data is able to improve both the nonadapted system and a system that optimises the above-mentioned weights only on the adaptation set.