AI-Driven Intelligent Detection and Correction of English Grammar Errors

AI-Driven Intelligent Detection and Correction of English Grammar Errors | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article AI-Driven Intelligent Detection and Correction of English Grammar Errors Yuxiang Nan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9232599/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Automatic Grammar Error Correction (GEC) is a classic NLP challenge that has been applied to language education, professional writing aid, and cross-lingual communication. Despite considerable progress with pre-trained Transformer architectures, high-performance GEC systems have three common weaknesses: lack of explicit syntactic dependency modelling, non-differentiated treatment of qualitatively different types of errors and imperfect ability to absorb post-deployment user feedback. In order to close these gaps, the present paper presents an end-to-end model, called HiGATGEC (Hierarchical Graph-Attention Transformer for Grammar Error Correction), that is built on a BERT encoder, a relation-conditioned two-layer Graph Attention Network (GAT) over Universal Dependency (UD) parse trees, an autoregressive decoder with a Grammar-Confidence Gate (GCG), and an online continual learning loop using Low-Rank Adaptation (LoRA). Errors are corrected at four linguistic levels: lexical, syntactic, semantic, and pragmatic. Experiments are performed on CoNLL-2014, BEA-2019 and JFLEG across five independent runs (mean ± SD), the contribution of each component is ablated, and statistical significance is assessed using a two-tailed paired t-test (α = 0.05). HiGATGEC achieves F0.5 = 67.83 ± 0.31 on CoNLL-2014 (p = 0.008 vs. GECToR-XL), F0.5 = 71.24 ± 0.28 on BEA-2019, and BLEU = 72.41 ± 0.44 on JFLEG, representing the highest scores among all evaluated baselines. Three simulated deployment cycles of online feedback further improved performance. These findings indicate that explicit syntactic graph modelling, hierarchical error weighting and dynamic feedback adaptation can yield reliable and repeatable improvements in GEC, offering a promising foundation for AI-aided writing systems in educational and professional settings. Grammar error correction Graph attention network BERT Transformer Multi-level error detection LoRA fine-tuning BEA-2019 CoNLL-2014 JFLEG Educational NLP Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 25 Apr, 2026 Reviewers agreed at journal 20 Apr, 2026 Reviewers agreed at journal 16 Apr, 2026 Reviewers invited by journal 14 Apr, 2026 Editor assigned by journal 26 Mar, 2026 Submission checks completed at journal 26 Mar, 2026 First submitted to journal 26 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9232599","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":626469927,"identity":"6292fd01-5ea1-4024-935d-ab4e91ab76a9","order_by":0,"name":"Yuxiang Nan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYFACHoYDHwxseOTZGxsffiBSC+PDGRVpcoY9h5uNJYjUwmzMc+awMcON9DYBHmI0mPOfPSbN28ac2DjzYRuDBIOdnG4DAS2WDefSJOe2sSW2Sye2PShgSDY2O0BAi8HBHjOJt208iY2zE9sNJBgOJG4jqOUwj5kEb5tEYsPNg20SPERpOcZjbMhzxgDofUZitZzhMQQGcgIwkBOBgWxAjF/OnzEARuV/YFQef/jwQ4WdHEEt6CaQpnwUjIJRMApGAQ4AAPRcRT2WIs27AAAAAElFTkSuQmCC","orcid":"","institution":"Jiaozuo University","correspondingAuthor":true,"prefix":"","firstName":"Yuxiang","middleName":"","lastName":"Nan","suffix":""}],"badges":[],"createdAt":"2026-03-26 10:09:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9232599/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9232599/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107704413,"identity":"713d02ca-f0e7-43a1-b5ac-7451fd8d95c5","added_by":"auto","created_at":"2026-04-24 08:45:16","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":597542,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9232599/v1_covered_c9b81bcb-13a4-4962-9588-bc6713427617.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"AI-Driven Intelligent Detection and Correction of English Grammar Errors","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-artificial-intelligence","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diai","sideBox":"Learn more about [Discover Artificial Intelligence](https://www.springer.com/44163)","snPcode":"","submissionUrl":"","title":"Discover Artificial Intelligence","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Grammar error correction, Graph attention network, BERT, Transformer, Multi-level error detection, LoRA fine-tuning, BEA-2019, CoNLL-2014, JFLEG, Educational NLP","lastPublishedDoi":"10.21203/rs.3.rs-9232599/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9232599/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAutomatic Grammar Error Correction (GEC) is a classic NLP challenge that has been applied to language education, professional writing aid, and cross-lingual communication. Despite considerable progress with pre-trained Transformer architectures, high-performance GEC systems have three common weaknesses: lack of explicit syntactic dependency modelling, non-differentiated treatment of qualitatively different types of errors and imperfect ability to absorb post-deployment user feedback. In order to close these gaps, the present paper presents an end-to-end model, called HiGATGEC (Hierarchical Graph-Attention Transformer for Grammar Error Correction), that is built on a BERT encoder, a relation-conditioned two-layer Graph Attention Network (GAT) over Universal Dependency (UD) parse trees, an autoregressive decoder with a Grammar-Confidence Gate (GCG), and an online continual learning loop using Low-Rank Adaptation (LoRA). Errors are corrected at four linguistic levels: lexical, syntactic, semantic, and pragmatic. 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