Multitask & Meta Learning for Language Models: Enhancing Aspect Based Sentiment Analysis

Multitask & Meta Learning for Language Models: Enhancing Aspect Based Sentiment Analysis | 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 Multitask & Meta Learning for Language Models: Enhancing Aspect Based Sentiment Analysis Swati Karni, Priyanka Avhad, Rahul Tyagi, Ayush Prasad, Sufiyan Ahmed Mujawar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7654632/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 This chapter presents a comprehensive investigation into improving Aspect Based Sentiment Analysis (ABSA) through multitask learning, meta learning, and task sampling strategies within the framework of language models. Leveraging state-of-the-art models like BERT and XLNet, the study explores the impact of pretraining tasks, particularly Next next-sentence prediction, on ABSA performance. Through meticulous experimentation and analysis, the efficacy of sampling tasks based on an importance subtask hierarchy is demonstrated, showcasing significant enhancements over state-of-the-art benchmarks. The findings underscore the importance of incorporating diverse tasks and sampling strategies for advancing ABSA and related natural language processing tasks, offering valuable insights for future research endeavors. Market Research Conversational Chatbot Sentiment Analysis Knowledge Graph Multimodal Systems Full Text Additional Declarations No competing interests reported. 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-7654632","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":524390102,"identity":"ba45b9c6-f209-44c0-b3f3-20bf89a2c7e0","order_by":0,"name":"Swati 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