Enhancing Contextual Semantic Reconstruction in College English Translation Using the BART Auto-Encoding Generation Framework
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Abstract
To address the decline in coherence and accuracy of college English translation caused by insufficient contextual semantic modeling, this paper proposes a Context-Aware Semantic Reconstruction Fine-tuning (CASR-F) method based on BART (Bidirectional and Auto-Regressive Transformers). The method is particularly useful for translating specialized terminology and complex technical descriptions in engineering fields such as electromagnetic wave analysis, antenna systems, and propagation scenario documentation. CASR-F introduces a dependency syntax-guided dynamic noise masking mechanism, which improves the encoder’s ability to identify core predicates and argument nodes through controlled noise injection. A gated semantic anchor attention module uses the top-level hidden state of the encoder as the semantic hub and dynamically adjusts cross-sentence information transmission weights through a gating function, thereby improving text-level semantic relation modeling during decoding. The model further enhances semantic recovery by jointly optimizing sequence reconstruction loss and cross-sentence semantic consistency constraints. Experiments show that CASR-F achieves a BLEU-4 score of 36.72 and a METEOR score of 31.45 in the College English Translation task, with a referential resolution accuracy of 78.6%. The F1 score for semantic role labeling on core argument A0 is improved to 83.9%. These results demonstrate the effectiveness of the proposed method in improving translation quality, reconstructing semantic structures, and enhancing semantic restoration, providing practical support for the accurate translation of electromagnetic engineering documents, antenna design descriptions, and wave propagation technical materials.
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References
B. Klimova, M. Pikhart, A. D. Benites, C. Lehr, and C. Sanchez-Stockhammer, “Neural machine translation in foreign language teaching and learning: A systematic review,” Education and Information Technologies, vol. 28, no. 1, pp. 663-682, 2023, doi: 10.1007/s10639-022-11194-2.
J. Son and B. Kim, “Translation performance from the user’s perspective of large language models and neural machine translation systems,” Information, vol. 14, no. 10, p. 574, 2023, doi: 10.3390/info14100574.
Y. Xiao, L. Wu, J. Guo, J. Li, M. Zhang, and T. Qin, “A survey on non-autoregressive generation for neural machine translation and beyond,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 10, pp. 11407-11427, 2023, doi: 10.1109/TPAMI.2023.3277122.
X. Lin, M. Afzaal, and H. S. Aldayel, “Syntactic complexity in legal translated texts and the use of plain English: a corpus-based study,” Humanities and Social Sciences Communications, vol. 10, no. 1, pp. 1-9, 2023, doi: 10.1057/s41599-022-01485-x.
N. M. Guerreiro, R. Rei, D. van Stigt, L. Coheur, P. Colombo, and A. F. T. Martins, “Xcomet: Transparent machine translation evaluation through fine-grained error detection,” Transactions of the Association for Computational Linguistics, vol. 12, pp. 979-995, 2024, doi: 10.1162/tacl_a_00683.
Y. Li, J. Li, J. Jiang, S. Tao, H. Yang, and M. Zhang, “P-transformer: Towards better document-to-document neural machine translation,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 31, pp. 3859-3870, 2023, doi: 10.1109/TASLP.2023.3313445.
R. He, C. Palominos, H. Zhang, M. F. Alonso-Sanchez, L. Palaniyappan, and W. Hinzen, “Navigating the semantic space: Unraveling the structure of meaning in psychosis using different computational language models,” Psychiatry Research, vol. 333, p. 115752, 2024, doi: 10.1016/j.psychres.2024.115752.
Y. A. Mohamed, A. Khanan, M. Bashir, A. H. H. M. Mohamed, M. A. E. Adiel, and M. Elsadig, “The impact of artificial intelligence on language translation: A review,” IEEE Access, vol. 12, pp. 25553-25579, 2024, doi: 10.1109/ACCESS.2024.3366802.
W. Xu, S. Agrawal, E. Briakou, M. J. Martindale, and M. Carpuat, “Understanding and detecting hallucinations in neural machine translation via model introspection,” Transactions of the Association for Computational Linguistics, vol. 11, pp. 546-564, 2023, doi: 10.1162/tacl_a_00563.
Y. Wang, “Artificial intelligence technologies in college English translation teaching,” Journal of Psycholinguistic Research, vol. 52, no. 5, pp. 1525-1544, 2023, doi: 10.1007/s10936-023-09960-5.
X. Zhao, Y. Deng, M. Yang, L. Wang, R. Zhang, H. Cheng, et al., “A comprehensive survey on relation extraction: Recent advances and new frontiers,” ACM Computing Surveys, vol. 56, no. 11, pp. 1-39, 2024, doi: 10.1145/3674501.
X. Liu, J. He, M. Liu, Z. Yin, L. Yin, and W. Zheng, “A scenario-generic neural machine translation data augmentation method,” Electronics, vol. 12, no. 10, p. 2320, 2023, doi: 10.3390/electronics12102320.
S. Chauhan and P. Daniel, “A comprehensive survey on various fully automatic machine translation evaluation metrics,” Neural Processing Letters, vol. 55, no. 9, pp. 12663-12717, 2023, doi: 10.1007/s11063-022-10835-4.
T. K. Lee, “Artificial intelligence and posthumanist translation: ChatGPT versus the translator,” Applied Linguistics Review, vol. 15, no. 6, pp. 2351-2372, 2024, doi: 10.1515/applirev-2023-0122.
M. H. A. Abdullah, N. Aziz, S. J. Abdulkadir, H. S. S. Alhassan Alhussian, and N. Talpur, “Systematic literature review of information extraction from textual data: Recent methods, applications, trends, and challenges,” IEEE Access, vol. 11, pp. 10535-10562, 2023, doi: 10.1109/ACCESS.2023.3240898.
H. Wang, X. Li, Z. Ren, M. Wang, and C. Ma, “Multimodal sentiment analysis representations learning via contrastive learning with condense attention fusion,” Sensors, vol. 23, no. 5, p. 2679, 2023, doi: 10.3390/s23052679.
S. Castilho and R. Knowles, “A survey of context in neural machine translation and its evaluation,” Natural Language Processing, vol. 31, no. 4, pp. 986-1016, 2025, doi: 10.1017/nlp.2024.7.
L. Kang, S. He, M. Wang, F. Long, and J. Su, “Bilingual attention based neural machine translation,” Applied Intelligence, vol. 53, no. 4, pp. 4302-4315, 2023, doi: 10.1007/s10489-022-03563-8.
S. Sharma, M. Diwakar, P. Singh, V. Singh, S. Kadry, and J. Kim, “Machine translation systems based on classical-statistical-deep-learning approaches,” Electronics, vol. 12, no. 7, pp. 1716-1722, 2023, doi: 10.3390/electronics12071716.
H. Mercan, Y. Akgun, and M. C. Odacioglu, “The evolution of machine translation: A review study,” International Journal of Language and Translation Studies, vol. 4, no. 1, pp. 104-116, 2024. Available from: https://izlik.org/JA28TD85GN.
H. Hu, X. Wang, Y. Zhang, Q. Chen, and Q. Guan, “A comprehensive survey on contrastive learning,” Neurocomputing, vol. 610, pp. 128645-128652, 2024, doi: 10.1016/j.neucom.2024.128645.
Y. Hao, T. Zhang, P. Zhao, Y. Liu, V. S. Sheng, and J. Xu, “Feature-level deeper self-attention network with contrastive learning for sequential recommendation,” IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 10, pp. 10112-10124, 2023, doi: 10.1109/TKDE.2023.3250463.
Y. Zhao, J. Zhang, and C. Zong, “Transformer: A general framework from machine translation to others,” Machine Intelligence Research, vol. 20, no. 4, pp. 514-538, 2023, doi: 10.1007/s11633-022-1393-5.
R. Patil, S. Boit, V. Gudivada, and J. Nandigam, “A survey of text representation and embedding techniques in nlp,” IEEE Access, vol. 11, pp. 36120-36146, 2023, doi: 10.1109/ACCESS.2023.3266377.
L. Yu, Y. Cheng, Z. Wang, V. Kumar, W. Macherey, and Y. Huang, “Spae: Semantic pyramid autoencoder for multimodal generation with frozen llms,” Advances in Neural Information Processing Systems, vol. 36, pp. 52692-52704, 2023, doi: 10.48550/arXiv.2306.17842.
L. Zhong, J. Wu, Q. Li, H. Peng, and X. Wu, “A comprehensive survey on automatic knowledge graph construction,” ACM Computing Surveys, vol. 56, no. 4, pp. 1-62, 2023, doi: 10.1145/3618295.
Z. Lu, R. Li, K. Lu, X. Chen, E. Hossain, and Z. Zhao, “Semantics-empowered communications: A tutorial-cum-survey,” IEEE Communications Surveys & Tutorials, vol. 26, no. 1, pp. 41-79, 2023, doi: 10.1109/COMST.2023.3333342.
M. Nagahisarchoghaei, N. Nur, L. Cummins, N. Nur, M. M. Karimi, and S. Nandanwar, “An empirical survey on explainable ai technologies: Recent trends, use-cases, and categories from technical and application perspectives,” Electronics, vol. 12, no. 5, pp. 1092-1098, 2023, doi: 10.3390/electronics12051092.
L. Xu, H. Xie, Z. Li, F. L. Wang, W. Wang, and Q. Li, “Contrastive learning models for sentence representations,” ACM Transactions on Intelligent Systems and Technology, vol. 14, no. 4, pp. 1-34, 2023, doi: 10.1145/3593590.
M. Apidianaki, “From word types to tokens and back: A survey of approaches to word meaning representation and interpretation,” Computational Linguistics, vol. 49, no. 2, pp. 465-523, 2023, doi: 10.1162/coli_a_00474.
I. D. Mienye, T. G. Swart, and G. Obaido, “Recurrent neural networks: A comprehensive review of architectures, variants, and applications,” Information, vol. 15, no. 9, pp. 517-524, 2024, doi: 10.3390/info15090517.
X. Zhao, L. Wang, Y. Zhang, X. Han, M. Deveci, and M. Parmar, “A review of convolutional neural networks in computer vision,” Artificial Intelligence Review, vol. 57, no. 4, pp. 99-105, 2024, doi: 10.1007/s10462-024-10721-6.
Z. Zhang, K. Chen, R. Wang, M. Utiyama, E. Sumita, and Z. Li, “Universal multimodal representation for language understanding,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 7, pp. 9169-9185, 2023, doi: 10.1109/TPAMI.2023.3234170.
J. Li, T. Tang, W. X. Zhao, J. Y. Nie, and J. R. Wen, “Pre-trained language models for text generation: A survey,” ACM Computing Surveys, vol. 56, no. 9, pp. 1-39, 2024, doi: 10.1145/3649449.
C. Zhou, C. Qiu, L. Liang, and D. E. Acuna, “Paraphrase identification with deep learning: A review of datasets and methods,” IEEE Access, vol. 13, pp. 65797-65822, 2025, doi: 10.1109/ACCESS.2025.3556899.