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Exploration of low-resource language-oriented machine translation system of genetic algorithm-optimized hyper-task network under cloud platform technology

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Abstract

To improve the quality and efficiency of machine translation (MT) for low-resource languages (LRLs), this paper analyzes the application of genetic algorithm (GA) under the background of cloud computing (CC). By using GA to optimize MT for LRL, this study aims to address the limitations and challenges of traditional methods on LRL. Firstly, GA under the background of the CC platform is analyzed in detail. The CC platform offers powerful computing and storage capabilities that provide the basis for large-scale optimization using GA. GA is an optimization algorithm based on the principle of biological evolution, which seeks the optimal solution by simulating genes’ genetic and evolutionary processes. In MT of LRL, GA can be used to optimize model parameters, select translation rules, and improve translation quality, thereby improving translation results. Secondly, the present situation of MT for LRL is described. Finally, the traditional LRL-oriented MT and the GA-optimized LRL-oriented MT are compared and analyzed. The results reveal that the traditional LRL-oriented MT has low accuracy and low customer satisfaction (CS). Considering the translation time, accuracy, and resource utilization rate (RUR), the highest rate is only about 47%. In contrast, GA-optimized LRL-oriented MT significantly improves translation accuracy and CS, reaching approximately 94%. The final evaluation results indicate that the accuracy of the model is usually above 70%, and up to 94%. Therefore, GA plays an important role in optimization for LRL-oriented MT. This paper provides technical support for optimizing LRL-oriented MT and contributes to the further development of MT technology. By applying GA, the limitations of traditional methods in LRL can be overcome and the quality and efficiency of translation can be improved. It is of great significance to improve the development of translation technology of LRL, enhance cross-language communication, and promote global cultural exchange.

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The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

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Acknowledgements

The authors acknowledge the help from the university colleagues.

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Authors and Affiliations

  1. College of Computer Science and Technology, South China University of Technology, Guangzhou, 510006, China

    Xiao Liu, Junlong Chen, Deyu Qi & Tong Zhang

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  1. Xiao Liu
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  2. Junlong Chen
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  3. Deyu Qi
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  4. Tong Zhang
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Contributions

XL contributed to conceptualization and writing—original draft preparation. JC was involved in writing—review and editing. DQ contributed to methodology and validation and provided software. TZ was involved in data curation and visualization. The author confirms being the sole contributor of this work and has approved it for publication.

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Correspondence to Junlong Chen.

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Liu, X., Chen, J., Qi, D. et al. Exploration of low-resource language-oriented machine translation system of genetic algorithm-optimized hyper-task network under cloud platform technology. J Supercomput 80, 3310–3333 (2024). https://doi.org/10.1007/s11227-023-05604-6

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