Bing YangThis email address is being protected from spambots. You need JavaScript enabled to view it.
School of English Language and Culture, Xi’an Fanyi University, Xi’an 710105, Shaanxi, China
Received: October 14, 2025 Accepted: December 18, 2025 Publication Date: February 1, 2026
Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.
The study proposes an enhanced English Translation Scoring System (ETSS) using an improved Generalized Maximum Probability Ratio (GLR) algorithm and an expanded BP neural network to improve translation evaluation accuracy. The system incorporates a three-phase method for text feature extraction, optimization, and fusion. Key improvements include wavelet packet decomposition for feature analysis and context-aware modifications for resolving ambiguities. The ETSS demonstrates over 95% accuracy in identity detection and an overall reliability of 92.3%, confirming its effectiveness for automated English translation assessment.
Keywords: English translation; GLR algorithm; BP neural network; evaluation system
[1] C. Manzoni and R. Ferrari, (2021) “Spatial transcriptomics: putting genome-wide expression on the map" Neuropsychopharmacology 45(1): 232–233. DOI: 10.1038/s41386-019-0484-7.
[2] Y. A. Mohamed, A. Khanan, M. Bashir, A. H. H. Mohamed, M. A. Adiel, and M. A. Elsadig, (2024) “The impact of artificial intelligence on language translation: a review" IEEE Access 12: 25553–25579. DOI: 10.1109/ACCESS.2024.3366802.
[3] A.Zumor, (2021) “Exploring intricacies in English passive construction translation in research articles’ abstracts by Arab author-translators" SAGE Open 11(3): 129 148. DOI: 10.1177/21582440211047556.
[4] N.Wang,(2025) “Edge computing based English translation model using fuzzy semantic optimal control technique" PLoS One 20(6): e0320481. DOI: 10.1371/journal.pone.0320481.
[5] J. Mu, Q. Zhang, and G. Yang, (2021) “A study on English translation tactics of the report on the work of the government 2021 from the perspective of eco-translatology" Open Access Library Journal 8(10): 9.
[6] D. Stoyanova, (2021) “Designing ESP courses: principles and specificities" Professional Discourse & Communication 3(1): 62–74. DOI: 10.24833/2687-0126 2021-3-1-62-74.
[7] G. A. Dress, Y. Regassa, and E. G. Koricho, (2025) “Model-Based Mechanical Property and Structural Failure Prediction of Pseudo Ductile Hybrid Composite" Journal of Building Material Science 7(2): DOI: 10.30564/jbms.v7i2.8642.
[8] Y. Fan and Y. Zang, (2024) “Exploration on the Practice of Translation Teaching for English Majors Based on the Internet of Things and Multimedia Assistance" Journal of Electrical Systems 20(4s): 363–374. DOI: 10.1155/2022/1399235.
[9] T.A.Mohammed,(2025)“EvaluatingTranslation Quality: A Qualitative and Quantitative Assessment of Ma chine and LLM-Driven Arabic–English Translations" In formation 16(6): 440. DOI: 10.3390/info16060440.
[10] S. Latif, F. Xian Wen, and L. L. Wang, (2021) “Intelligent decision support system approach for predicting the performance of students based on three-level machine learning technique" Journal of Intelligent Systems 30(1): 739–749. DOI: 10.1515/jisys-2020-0065.
[11] C. Li, (2022) “A Study on Chinese–English Machine Translation Based on Transfer Learning and Neural Net works" Wireless Communications and Mobile Computing 2022(1): 8282164. DOI: 10.1155/2022/8282164.
[12] S. Chauhan and P. Daniel, (2023) “A comprehensive survey on various fully automatic machine translation evaluation metrics" Neural Processing Letters 55(9): 12663–12717. DOI: 10.1007/s11063-022-10835-4.
[13] N.S. Allur and R. Hemnath, (2018) “A hybrid frame work for automated test case generation and optimization using pre-trained language models and genetic programming" International Journal of Engineering Re search and Science & Technology 14(3): 89–97.
[14] X. Li and C. Huang, (2025) “Design of an intelligent grading system for college English translation based on big data technology" Systems and Soft Computing 7: 200205. DOI: 10.1016/j.sasc.2025.200205.
[15] Y. Li, Y. Wu, and G. Zhu, (2024) “Automatic rating method based on deep transfer learning for machine translation considering contextual semantic awareness" Alexandria Engineering Journal 105: 588–597. DOI: 10.1016/j.aej.2024.08.046.
[16] X. Liang, W. Cheng, C. Zhang, L. Wang, X. Yan, and Q. Chen, (2023) “YOLOD: A task decoupled network based on YOLOv5" IEEE Transactions on Consumer Electronics 69(4): 775–785. DOI: 10.1109/TCE.2023.3278264.
[17] M.Gong, (2024) “The neural network algorithm-based quality assessment method for university English translation" Network: Computation in Neural Systems: 1–13. DOI: 10.1080/0954898X.2024.2338446.
[18] J. Li, (2024) “English–Chinese translation quality assessment based on phrase statistical machine translation de coding algorithm" International Journal of Maritime Engineering 1(1): 675–688. DOI: 10.5750/ijme.v1i1.1395.
[19] Y. Yuan and S. Sharoff, (2020) “Sentence level human translation quality estimation with attention-based neural networks" arXiv preprint arXiv:2003.06381: DOI: 10. 48550/arXiv.2003.06381.
[20] X. Jiang, (2022) “Research on the analysis of correlation factors of English translation ability improvement based on deep neural network" Computational Intelligence and Neuroscience 2022(1): 9345354. DOI: 10.1155/2022/9345354.
[21] L. Tingting and X. Mengyu, (2020) “Analysis and evaluation on the quality of news text machine translation based on neural network" Multimedia Tools and Ap plications 79(23): 17015–17026. DOI: 10.1007/s11042 019-7532-5.
[22] M. A. A. Albadr, S. Tiun, and F. T. Al-Dhief, (2018) “Evaluation of machine translation systems and related procedures" ARPN Journal of Engineering and Applied Sciences 13(12): 3961–3972.
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