Article: Real-time detection of Business English grammar errors driven by transfer learning Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.42 pp.35 - 50 Abstract: Improving the grammatical accuracy of Business English writing is crucial, but general grammar checking tools often struggle to adapt to professional contexts. This study proposes a real-time grammar error detection method based on BERT transfer learning, aimed at enhancing performance in business scenarios. Methodologically, the BERT-base pre-trained model is directly utilised to capture general language features. To meet real-time requirements, a lightweight model inference architecture was designed. Experimental results show that the model fine-tuned for the business domain achieves an accuracy rate of 89.2% and an F1 score of 0.842. The improvements are particularly significant in detecting formal expressions and complex sentence structures specific to business texts. This study demonstrates that combining BERT-based transfer learning with fine-tuning using small yet representative domain-specific datasets can effectively enhance the practicality and accuracy of grammar error detection in Business English. Inderscience Publishers - linking academia, business and industry through research

Title: Real-time detection of Business English grammar errors driven by transfer learning

Authors: Zhenxin Fang; Zhenyu Song

Addresses: School of Education, Shanghai Industrial and Commercial Polytechnic, Shanghai, 201806, China ' Smart Fire Protection Division, Shanghai Yida Communication Co., Ltd, Shanghai, 200082, China

Abstract: Improving the grammatical accuracy of Business English writing is crucial, but general grammar checking tools often struggle to adapt to professional contexts. This study proposes a real-time grammar error detection method based on BERT transfer learning, aimed at enhancing performance in business scenarios. Methodologically, the BERT-base pre-trained model is directly utilised to capture general language features. To meet real-time requirements, a lightweight model inference architecture was designed. Experimental results show that the model fine-tuned for the business domain achieves an accuracy rate of 89.2% and an F1 score of 0.842. The improvements are particularly significant in detecting formal expressions and complex sentence structures specific to business texts. This study demonstrates that combining BERT-based transfer learning with fine-tuning using small yet representative domain-specific datasets can effectively enhance the practicality and accuracy of grammar error detection in Business English.

Keywords: transfer learning; Business English; grammar error detection; BERT.

DOI: 10.1504/IJICT.2025.150142

International Journal of Information and Communication Technology, 2025 Vol.26 No.42, pp.35 - 50

Received: 11 Aug 2025
Accepted: 01 Oct 2025
Published online: 01 Dec 2025 *

Title: Real-time detection of Business English grammar errors driven by transfer learning

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