Advertisement

Particle Swarm Optimization as a New Measure of Machine Translation Efficiency

  • José Angel Montes OlguínEmail author
  • Jolanta Mizera-Pietraszko
  • Ricardo Rodriguez Jorge
  • Edgar Alonso Martínez García
Conference paper
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 

Notes

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.

References

  1. 1.
    Hutchins, W.J.: Machine translation: a brief history. In: Concise History of the Language Sciences: From the Sumerians to the Cognitivists, pp. 431–445 (1995)Google Scholar
  2. 2.
    Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing, 2nd edn. Springer, Heidelberg (2015)Google Scholar
  3. 3.
    Otto, E., Riff, M.C.: EDA: an evolutionary decoding algorithm for statistical machine translation. Appl. Artif. Intell. 21(7), 605–621 (2007)CrossRefGoogle Scholar
  4. 4.
    Ameur, D., David, L., Kamel, S.: Genetic-Based Decoder. Lecture Notes in Computer Science (2016)Google Scholar
  5. 5.
    Menai, M.E.B.: Word sense disambiguation using evolutionary algorithms – application to Arabic language. Comput. Hum. Behav. 41, 92–103 (2014)CrossRefGoogle Scholar
  6. 6.
    Mihalcea, R., Corley, C., Strapparava, C.: Corpus-based and knowledge-based measures of text semantic similarity. In: American Association for Artificial Intelligence, pp. 775–780 (2006)Google Scholar
  7. 7.
    Dehak, N., Dehak, R., Glass, J., Reynolds, D., Kenny, P.: Cosine similarity scoring without score normalization techniques. In: De Odyssey 2010, Brno (2010)Google Scholar
  8. 8.
    Kazemi, A., Toral, A., Way, A., Monadjemi, A., Nematbakhsh, M.: Syntax- and semantic-based reordering in hierarchical phrase-based statistical machine translation. Expert Syst. Appl. 84, 186–199 (2017)CrossRefGoogle Scholar
  9. 9.
    Choi, H., Cho, K., Bengio, Y.: Context-dependent word representation for neural machine translation. Comput. Speech Lang. 45, 149–160 (2017)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • José Angel Montes Olguín
    • 1
    Email author
  • Jolanta Mizera-Pietraszko
    • 2
  • Ricardo Rodriguez Jorge
    • 3
  • Edgar Alonso Martínez García
    • 3
  1. 1.Instituto Tecnológico Superior Zacatecas NorteRío GrandeMexico
  2. 2.Opole UniversityOpolePoland
  3. 3.Universidad Autonoma de Ciudad JuarezChihuahuaMexico

Personalised recommendations