The Journal of Machine Learning Research
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
An empirical study of smoothing techniques for language modeling
ACL '96 Proceedings of the 34th 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
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
A general regression technique for learning transductions
ICML '05 Proceedings of the 22nd international conference on Machine learning
The Alignment Template Approach to Statistical Machine Translation
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
Kernel regression based machine translation
NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
L1 regularized regression for reranking and system combination in machine translation
WMT '10 Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR
Similarity word-sequence kernels for sentence clustering
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Instance selection for machine translation using feature decay algorithms
WMT '11 Proceedings of the Sixth Workshop on Statistical Machine Translation
RegMT system for machine translation, system combination, and evaluation
WMT '11 Proceedings of the Sixth Workshop on Statistical Machine Translation
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We present a machine translation framework based on Kernel Regression techniques. The translation process is modeled as a string-to-string mapping. For doing so, first both source and target strings are mapped to a natural vector space obtaining feature vectors. Afterwards, a translation mapping is defined from the source feature vector to the target feature vector. This translation mapping is learnt by linear regression. Once the target feature vector is obtained, we use a multi-graph search to find all the possible target strings whose mappings correspond to the "translated" feature vector. We present experiments in a small but relevant task showing encouraging results.