A statistical approach to machine translation
Computational Linguistics
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
Decoding complexity in word-replacement translation models
Computational Linguistics
An efficient method for determining bilingual word classes
EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
An empirical study of smoothing techniques for language modeling
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Fast and optimal decoding for machine translation
Artificial Intelligence
Word re-ordering and DP-based search in statistical machine translation
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
HMM-based word alignment in statistical translation
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
Improved statistical alignment models
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Generation of word graphs in statistical machine translation
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
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In a statistical machine translation system (SMTS), decoding is the process of finding the most likely translation based on a statistical model, according to previously learned parameters. The success of an SMTS is strongly dependent on the quality of its decoder. Most of the SMTS's published in current literature use approaches based on traditional optimization methods and heuristics. On the other hand, over the last few years there has been a rapid increase in the use of metaheuristics. These kinds of techniques have shown to be able to solve difficult search problems in an efficient way for a wide number of applications. This paper proposes a new approach based on evolutionary hybrid algorithms to translate sentences in a specific technical context. The algorithm has been enhanced by adaptive parameter control. The tests are carried out in the context of Spanish and then translated to English. The experimental results validate the superior performance of our method in contrast to a statistical greedy decoder. We also compare our new approach to the existing public domain general translators.