A statistical approach to machine translation
Computational Linguistics
Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis
Artificial Intelligence Review
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Machine translation method using inductive learning with genetic algorithms
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Optimization of word alignment clues
Natural Language Engineering
A Hybrid Approach to Sentence Alignment Using Genetic Algorithm
ICCTA '07 Proceedings of the International Conference on Computing: Theory and Applications
METIS-II: low resource machine translation
Machine Translation
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
Joint optimization for machine translation system combination
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Real-parameter genetic algorithms for finding multiple optimal solutions in multi-modal optimization
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Pattern matching-based system for machine translation (MT)
SETN'06 Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence
Implementing a language-independent MT methodology
MM '12 Proceedings of the First Workshop on Multilingual Modeling
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In this paper, an automated method is proposed for optimising the real-valued parameters of a hybrid Machine Translation (MT) system that employs pattern recognition techniques together with extensive monolingual corpora in the target language from which statistical information is extracted. The absence of a parallel corpus prohibits the use of the training techniques traditionally employed in state-of-the-art Statistical Machine Translation systems. The proposed approach for fine-tuning the system parameters towards the generation of high-quality translations is based on a Genetic Algorithm and the multi-objective evolutionary algorithm SPEA2. In order to evaluate the translation quality, established MT automatic evaluation criteria are employed, such as BLEU and METEOR. Furthermore, various ways of combining these criteria are explored, in order to exploit each one's characteristics and evaluate the produced translations. The experimental results indicate the effectiveness of this approach, since the translation quality of the evaluation sentence sets used is substantially improved in all studied configurations, when compared to the output of the same system operating with manually-defined parameters. Out of all configurations, the multi-objective evolutionary algorithms, combining several MT evaluation metrics, are found to produce the highest quality translations.