Construction of a cross-domain machime translation model based on meta-learing and semlantic transfer

Construction of a cross-domain machime translation model based on meta-learing and semlantic transfer | 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 Construction of a cross-domain machime translation model based on meta-learing and semlantic transfer Yongjian Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8484220/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In recent years, neural machine translation has significantly progressed on standard corpora. However, it still faces considerable performance degradation under domain differences between training and test corpora, manifested in problems such as semantic drift, terminology mistranslation, and style imbalance. To meet this challenge, this paper proposes a cross-domain neural translation framework that integrates meta-learning and semantic transfer mechanisms, combining language function modeling and Transformer semantic encoding to improve the model's adaptability and semantic alignment ability in low-resource target domains. The method introduces a task-level meta-learning strategy to achieve fast migration and combines contrastive learning to optimize semantic space consistency. Empirical evaluation is carried out on five cross-domain datasets, OPUS and IWSLT. The proposed model is better than eight mainstream methods regarding BLEU, TER, and CHRF, and its average improvement in BLEU is 1.1 ~ 2.3 points. Further experiments show that after introducing chapter tags and perturbation mechanisms, the model shows stronger robustness in long texts, terminology-intensive corpora, and style-switching scenarios. This study provides a modeling reference with a clear structure, theory-driven, and transferable for cross-domain translation in complex registers. Linguistics Cross-domain translation Meta-learning Semantic transfer Neural machine translation Functional linguistics Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8484220","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":567493659,"identity":"89398f59-1c75-49b2-883d-e5784e7a22da","order_by":0,"name":"Yongjian 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