Title: Automatic error calibration system for English semantic translation based on machine learning

Authors: Zhenhua Wei

Addresses: Yongcheng Vocational College, Yongcheng 476600, Henan Province, China

Abstract: The traditional English semantic translation error calibration system can not determine the optimal translation solution, which has the problems of high CPU utilisation, low translation accuracy and high calibration time-consuming. Before English semantic translation, English semantic features are decomposed to realise fuzzy mapping selection of English semantic translation. Then, English semantic translation decision function is obtained by constructing semantic ontology model, while English semantic translation error automatic calibration algorithm is realised by machine learning algorithm. Finally, the overall architecture and network topology of the system is designed, and the design of automatic proofreading system of English semantic translation errors is completed. The experimental results show that the running time of the proposed system is 1.5 s, the CPU occupancy rate of the designed system is only 0.9%, and the calibration accuracy is as high as 99%.

Keywords: dimensionless treatment; calibration algorithm; high CPU utilisation; low translation accuracy; high calibration time-consuming; machine learning; translation decision function.

DOI: 10.1504/IJISE.2023.129141

International Journal of Industrial and Systems Engineering, 2023 Vol.43 No.3, pp.301 - 316

Received: 07 Dec 2020
Accepted: 17 Mar 2021
Published online: 22 Feb 2023 *

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