Model and Evaluation: Towards Fairness in Multilingual Text Classification

Model and Evaluation: Towards Fairness in Multilingual Text Classification | 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 Model and Evaluation: Towards Fairness in Multilingual Text Classification Nankai Lin, Junheng He, Zhenghang Tang, Jiajun Fang, Aimin Yang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5018458/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Feb, 2026 Read the published version in International Journal of Machine Learning and Cybernetics → Version 1 posted 12 You are reading this latest preprint version Abstract Recently, more and more research has focused on addressing bias in text classification models. However, existing research mainly focuses on the fairness of monolingual text classification models, and research on fairness for multilingual text classification is still very limited. In this paper, we focus on the task of multilingual text classification and propose a debiasing framework for multilingual text classification based on contrastive learning. Our proposed method does not rely on any external language resources and can be extended to any other languages. In addition, the existing research on the fairness of multilingual text classification is relatively simple in the evaluation mode. The evaluation method of fairness is the same as the monolingual equality difference evaluation method, that is, the evaluation is performed on a single language. We propose a multi-dimensional fairness evaluation framework for multilingual text classification, which evaluates the model’s monolingual equality difference, multilingual equality difference, multilingual equality performance difference, and negative gain of the fairness strategy. We hope that our work can provide a more general debiasing method and a more comprehensive evaluation framework for multilingual text fairness tasks. Multilingual Text Classification Debiasing Framework Multi-dimensional Fairness Evaluation Framework Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 02 Feb, 2026 Read the published version in International Journal of Machine Learning and Cybernetics → Version 1 posted Editorial decision: Revision requested 28 May, 2025 Reviews received at journal 05 May, 2025 Reviewers agreed at journal 29 Apr, 2025 Reviews received at journal 23 Apr, 2025 Reviewers agreed at journal 26 Mar, 2025 Reviewers agreed at journal 08 Feb, 2025 Reviewers agreed at journal 08 Feb, 2025 Reviewers agreed at journal 14 Sep, 2024 Reviewers invited by journal 09 Sep, 2024 Editor assigned by journal 06 Sep, 2024 Submission checks completed at journal 04 Sep, 2024 First submitted to journal 02 Sep, 2024 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. 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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-5018458","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":353951598,"identity":"ac096d9d-7aac-4dce-a629-bb4c6272f5ee","order_by":0,"name":"Nankai Lin","email":"","orcid":"","institution":"Guangdong University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Nankai","middleName":"","lastName":"Lin","suffix":""},{"id":353951599,"identity":"23644941-8d5b-4a94-852a-ad3570249014","order_by":1,"name":"Junheng He","email":"","orcid":"","institution":"Guangdong University of 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