Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
A systematic comparison of various statistical alignment models
Computational Linguistics
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
Improved statistical alignment models
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
An evaluation exercise for word alignment
HLT-NAACL-PARALLEL '03 Proceedings of the HLT-NAACL 2003 Workshop on Building and using parallel texts: data driven machine translation and beyond - Volume 3
The equation for response to selection and its use for prediction
Evolutionary Computation
Word alignment for languages with scarce resources
ParaText '05 Proceedings of the ACL Workshop on Building and Using Parallel Texts
How evolutionary algorithms are applied to statistical natural language processing
Artificial Intelligence Review
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In statistical machine translation, an alignment defines a mapping between the words in the source and in the target sentence. Alignments are used, on the one hand, to train the statistical models and, on the other, during the decoding process to link the words in the source sentence to the words in the partial hypotheses generated. In both cases, the quality of the alignments is crucial for the success of the translation process. In this paper, we propose an algorithm based on an Estimation of Distribution Algorithm for computing alignments between two sentences in a parallel corpus. This algorithm has been tested on different tasks involving different pair of languages. In the different experiments presented here for the two word-alignment shared tasks proposed in the HLT-NAACL 2003 and in the ACL 2005, the EDA-based algorithm outperforms the best participant systems.