Dependency networks for inference, collaborative filtering, and data visualization
The Journal of Machine Learning Research
Deriving transfer rules from dominance-preserving alignments
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
An empirical study of smoothing techniques for language modeling
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Chart-based transfer rule application in Machine Translation
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
A method for distinguishing exceptional and general examples in example-based transfer systems
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
Learning translation templates from bilingual text
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Finding structural correspondences from bilingual parsed corpus for corpus-based translation
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Overcoming the customization bottleneck using example-based MT
DMMT '01 Proceedings of the workshop on Data-driven methods in machine translation - Volume 14
Inducing lexico-structural transfer rules from parsed Bi-texts
DMMT '01 Proceedings of the workshop on Data-driven methods in machine translation - Volume 14
DMMT '01 Proceedings of the workshop on Data-driven methods in machine translation - Volume 14
A Bayesian approach to learning Bayesian networks with local structure
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
MSR-MT: The Microsoft Research Machine Translation System
AMTA '02 Proceedings of the 5th Conference of the Association for Machine Translation in the Americas on Machine Translation: From Research to Real Users
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One of the problems facing translation systems that automatically extract transfer mappings (rules or examples) from bilingual corpora is the trade-off between contextual specificity and general applicability of the mappings, which typically results in conflicting mappings without distinguishing context. We present a machine-learning approach to choosing between such mappings, using classifiers that, in effect, selectively expand the context for these mappings using features available in a linguistic representation of the source language input. We show that using these classifiers in our machine translation system significantly improves the quality of the translated output. Additionally, the set of distinguishing features selected by the classifiers provides insight into the relative importance of the various linguistic features in choosing the correct contextual translation.