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Particle Swarm Optimization as a New Measure of Machine Translation Efficiency

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Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 737)

Abstract

The present work proposes a new approach to measuring efficiency of evolutionary algorithm-based Machine Translation. We implement some attributes of evolutionary algorithms performing cosine similarity objective function of a Particle Swarm Optimization (PSO) algorithm then, we evaluate an English text set for translation precision into the Spanish text as a simulated benchmark, and explore the backward process. Our results show that PSO algorithm can be used for translation of multiple language sentences with one identifier only, in other words the technology presented is language-pair independent. Specifically, we indicate that our cosine similarity objective function improves the velocity attribute of the PSO algorithm, making the complex cost functions unnecessary.

Keywords

  • Evolutionary algorithms
  • Machine Translation
  • Cosine similarity

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  • DOI: 10.1007/978-3-319-76357-6_16
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Acknowledgements

The project is supported by a research grant No. DSA/103.5/16/10473 awarded by PRODEP and by Autonomous University of Ciudad Juarez in Mexico. Title - Detection of Cardiac Arrhythmia Patterns through Adaptive Analysis.

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Correspondence to José Angel Montes Olguín .

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Montes Olguín, J.A., Mizera-Pietraszko, J., Rodriguez Jorge, R., Martínez García, E.A. (2018). Particle Swarm Optimization as a New Measure of Machine Translation Efficiency. In: Abraham, A., Haqiq, A., Muda, A., Gandhi, N. (eds) Proceedings of the Ninth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2017). SoCPaR 2017. Advances in Intelligent Systems and Computing, vol 737. Springer, Cham. https://doi.org/10.1007/978-3-319-76357-6_16

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  • DOI: https://doi.org/10.1007/978-3-319-76357-6_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-76356-9

  • Online ISBN: 978-3-319-76357-6

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