Article: Generative adversarial network and grammar rule constraint optimisation for English interlanguage error correction Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.56 pp.91 - 110 Abstract: Aiming at the problems of semantic distortion and over-correction that often occur in the text error correction of English learners by existing generative models, this paper accordingly proposes a novel generative adversarial network method - integrating grammatical rule constraints (generative adversarial networks with grammatical rule constraints). By introducing formal grammatical knowledge as a flexible constraint into the network training process, the model is effectively guided to correct errors while better maintaining the original meaning and overall fluency of sentences. Experiments conducted on the public learner corpus show that this method significantly increases the error correction accuracy by 12% and effectively reduces the number of over-correction cases by 16%. The research thereby provides an effective way to solve the persistent balance problem between accuracy and naturalness in automatic grammar correction. Inderscience Publishers - linking academia, business and industry through research

Title: Generative adversarial network and grammar rule constraint optimisation for English interlanguage error correction

Authors: Liu Liu

Addresses: School of Humanity and Education, Guangxi Financial Vocational College, Nanning, 530007, China

Abstract: Aiming at the problems of semantic distortion and over-correction that often occur in the text error correction of English learners by existing generative models, this paper accordingly proposes a novel generative adversarial network method - integrating grammatical rule constraints (generative adversarial networks with grammatical rule constraints). By introducing formal grammatical knowledge as a flexible constraint into the network training process, the model is effectively guided to correct errors while better maintaining the original meaning and overall fluency of sentences. Experiments conducted on the public learner corpus show that this method significantly increases the error correction accuracy by 12% and effectively reduces the number of over-correction cases by 16%. The research thereby provides an effective way to solve the persistent balance problem between accuracy and naturalness in automatic grammar correction.

Keywords: English interlanguage correction; generative adversarial network; grammatical rule constraints; semantic retention; excessive correction.

DOI: 10.1504/IJICT.2026.153907

International Journal of Information and Communication Technology, 2026 Vol.27 No.56, pp.91 - 110

Received: 12 Jan 2026
Accepted: 12 Feb 2026
Published online: 29 May 2026 *

Title: Generative adversarial network and grammar rule constraint optimisation for English interlanguage error correction

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