Business English Translation Quality Assessment Using Multimodal BERT

Main Article Content

S. J. Xiang

Abstract

Business translation quality assessment faces numerous challenges, particularly for professional technical documents in engineering and industrial applications, where accurate terminology, numerical information, images, and structured tables are essential for reliable information transmission. Such multimodal consistency is also critical for documentation management and knowledge exchange in advanced electromagnetic systems and communication engineering. To address these issues, this paper proposes a multimodal BERT framework for no-reference translation quality assessment. The framework jointly integrates three modalities: text (source document and translation), images, and structured tables. Textual semantics are encoded using BERT, visual features are extracted by ResNet50, and tabular information is represented through TUTA. A multi-head cross-modal attention mechanism is then employed for lexical and cell alignment, while gated weighted pooling adaptively fuses heterogeneous modal representations to regress a unified translation quality score. To validate the proposed approach, a multimodal business dataset is constructed from publicly available reports issued by State Grid Corporation of China and Sinopec during 2023–2024, comprising approximately 12,000 documents, 48,000 sentence pairs, 20,000 image fragments, and 15,000 structured tables, with gold-standard annotations generated using an extended MQM evaluation scheme. Experimental results demonstrate that the proposed method achieves an RMSE of 0.06 for sentence-level quality regression, with Spearman and Pearson correlations of 0.87 and 0.88, respectively, relative to human assessment. In simulated business evaluation scenarios, the framework reduces economic risk by 38.64% and lowers the customer complaint rate to 3.21 %. These results demonstrate that multimodal BERT provides an effective and verifiable solution for translation quality assessment while offering valuable support for multilingual technical documentation, electromagnetic engineering communication, and intelligent information management in complex industrial environments.

Downloads

Download data is not yet available.

Article Details

How to Cite
Xiang, S. J. (2026). Business English Translation Quality Assessment Using Multimodal BERT. Advanced Electromagnetics, 15(3), 5126–5139. https://doi.org/10.7716/aem.v15i3.3570
Section
Research Articles

References

D. Tang, “Study on the English-Chinese Translation of Business Contracts from the Perspective of Skopos Theory,” Journal of Theory and Practice in Linguistics, vol. 1, no. 3, pp. 1-9, 2024.

S. Liu, Y. Chen, K. Xu, and J. Lin, “Emotional analysis of evaluation discourse in business English translation based on language big data mining of public health environment,” Frontiers in Public Health, vol. 10, Art. no. 981182, 2022, doi: 10.3389/FPUBH.2022.981182.

View Article

V. Agustina, R. Thamrin N, and E. Oktoma, “The role of English language proficiency in the global economy and business communication,” International Journal Administration, Business & Organization, vol. 5, no. 4, pp. 82-90, 2024, doi: 10.61242/ijabo.24.423.

View Article

G. Tang, O. Yousuf, and Z. Jin, “Improving BERTScore for machine translation evaluation through contrastive learning,” IEEE Access, vol. 12, no. 1, pp. 77739-77749, 2024, doi: 10.1109/ACCESS.2024.3406993.

View Article

D. Beauchemin, H. Saggion, and R. Khoury, “MeaningBERT: assessing meaning preservation between sentences,” Frontiers in Artificial Intelligence, vol. 6, no. 6, Art. no. 1223924, 2023, doi: 10.3389/frai.2023.1223924.

View Article

C. Han, “Quality assessment in multilingual, multimodal, and multiagent translation and interpreting (QAM3 T&I): Proposing a unifying framework for research,” Interpreting and Society, vol. 5, no. 1, pp. 27-55, 2025, doi: 10.1177/27523810251322645.

View Article

T. Tayir and L. Li, “Unsupervised multimodal machine translation for low-resource distant language pairs,” ACM Transactions on Asian and Low-Resource Language Information Processing, vol. 23, no. 4, pp. 1-22, 2024, doi: 10.18653/v1/2024.findings-emnlp.320.

View Article

N. M. Gardazi, A. Daud, M. K. Malik, A. Bukhari, T. Alsahfi, and B. Al-shemaimri, “BERT applications in natural language processing: a review,” Artificial Intelligence Review, pp. 58(6):, 2025, doi: 1-49.10.1007/s10462-025-11162-5.

View Article

Y. Cui and M. Liang, “Automated scoring of translations with BERT models: Chinese and English language case study,” Applied Sciences, vol. 14, no. 5, pp. 1925-1941, 2024, doi: 10.3390/APP14051925.

View Article

Z. Xiao, X. Ning, and M. J. M. Duritan, “BERT-SVM: A hybrid BERT and SVM method for semantic similarity matching evaluation of paired short texts in English teaching,” Alexandria Engineering Journal, vol. 126, no. 1, pp. 231-246, 2025, doi: 10.1016/J.AEJ.2025.04.061.

View Article

D. Guo, “Deep learning-driven context-aware English translation for ambiguous sentences,” International Journal of Information and Communication Technology, vol. 26, no. 15, pp. 41-56, 2025, doi: 10.1504/IJICT.2025.146373.

View Article

M. Alamri and S. Lajmi, “Design a smart platform translating Arabic sign language to English language,” Int. J. Electr. Comput. Eng. IJECE, vol. 14, no. 4, pp. 4759-4774, 2024, doi: 10.11591/ijece.v14i4.pp4759-4774.

View Article

V. D. Avina, M. Amiruzzaman, S. Amiruzzaman, L. B. Ngo, and M. A. A. Dewan, “An AI-Based Framework for Translating American Sign Language to English and Vice Versa,” Information, vol. 14, no. 10, pp. 569-579, 2023, doi: 10.3390/info14100569.

View Article

M. Roweida, A. Inad, M. Al-Ayyoub, and A. Fadel, “Multimodal Multi-source Neural Machine Translation: Building Resources for Image Caption Translation from European Languages into Arabic,” Computation, vol. 13, no. 8, pp. 194-204, 2025, doi: 10.3390/computation13080194.

View Article

E. Tian, Z. Zhu, F. Liu, Z. Li, R. Gu, and S. Zhao, “Multimodal Machine Translation Based on Enhanced Knowledge Distillation and Feature Fusion,” Electronics, vol. 13, no. 15, pp. 3084-3099, 2024, doi: 10.32604/cmc.2025.061145.

View Article

H. Yu, R. Ma, M. Su, P. An, and K. Li, “A novel deep translated attention hashing for cross-modal retrieval,” Multimedia Tools and Applications, vol. 81, no. 18, pp. 26443-26461, 2022, doi: 10.1007/s11042-022-12860-w.

View Article

R. Cai, J. Dong, T. Liang, Y. Liang, Y. Wang, X. Yang, et al., “Cross-lingual cross-modal retrieval with noise-robust fine-tuning,” IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 11, pp. 5860-5873, 2024, doi: 10.1109/TKDE.2024.3400060.

View Article

A. Shin, M. Ishii, and T. Narihira, “Perspectives and prospects on transformer architecture for cross-modal tasks with language and vision,” International journal of computer vision, vol. 130, no. 2, pp. 435-454, 2022, doi: 10.1007/s11263-021-01547-8.

View Article

J. Guo, J. Ye, Y. Xiang, and Z. Yu, “Layer-level progressive transformer with modality difference awareness for multi-modal neural machine translation,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 31, no. 1, pp. 3015-3026, 2023, doi: 10.1109/TASLP.2023.3301210.

View Article

M. Reyad, A. M. Sarhan, and M. Arafa, “A modified Adam algorithm for deep neural network optimization,” Neural Computing and Applications, vol. 35, no. 23, pp. 17095-17112, 2023, doi: 10.1007/S00521-023-08568-Z.

View Article

I. V. Modoranu, M. Safaryan, G. Malinovsky, E. Kurtic, T. Robert, P. Richtarik, et al., “Microadam: Accurate adaptive optimization with low space overhead and provable convergence,” Advances in Neural Information Processing Systems, vol. 37, no. 1, pp. 1-43, 2024.

C. Zhang, Y. Shao, H. Sun, L. Xing, Q. Zhao, and L. Zhang, “The WuC-Adam algorithm based on joint improvement of Warmup and cosine annealing algorithms,” Mathematical Biosciences & Engineering, vol. 21, no. 1, pp. 1270-1285, 2024, doi: 10.3934/mbe.2024054.

View Article

O. V. Johnson, C. Xinying, K. W. Khaw, and M. H. Lee, “ps-CALR: periodic-shift cosine annealing learning rate for deep neural networks,” IEEE Access, vol. 11, no. 1, pp. 139171-139186, 2023.

Similar Articles

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 > >> 

You may also start an advanced similarity search for this article.