Sarcasm Detection with Contextual-Representation Multihop-Attention network

Sarcasm Detection with Contextual-Representation Multihop-Attention network | 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 Article Sarcasm Detection with Contextual-Representation Multihop-Attention network Yufeng Diao, Xueqian Su, Shiqi Li, Hao Zhang, Xiaochao Fan, Xiaoyu Liu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6539217/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Sarcasm is a delicate and implicit form of expression where what people say often runs counter to what they really mean. With this intentional ambiguity and complexity, sarcasm detection has always been a challenging task, even for humans. The importance of sarcasm detection has been realized by industry and academia. However, existing methodologies for automatic sarcasm detection predominantly rely on lexical and linguistic indicators, which prove to be insufficiently effective in addressing the complexities of sarcasm recognition.This paper focuses on tackling the challenging problem of sarcasm detection by leveraging a novel Contextual-Representation Multihop-Attention (CRMA) network based on bidirectional GRU. CRMA not only understands the ambiguity of sarcasm by the combination of contextual representation and word representation, but also learns the semantic complexity among sentences by the multihop attention mechanism. We perform comprehensive experimental evaluations on IAC dataset collected from popular website.The empirical findings reveal that our proposed framework achieves a substantial performance advantage over existing cutting-edge approaches, while supplementary case analyses further corroborate the robustness of our network architecture in identifying sarcastic expressions. Physical sciences/Mathematics and computing/Computational science Physical sciences/Mathematics and computing/Computer science Sarcasm Detection Contextual-Representation Multihop-Attention Ambiguity Complexity Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 14 Jun, 2025 Reviewers invited by journal 13 Jun, 2025 Editor assigned by journal 13 Jun, 2025 Editor invited by journal 19 May, 2025 Submission checks completed at journal 18 May, 2025 First submitted to journal 27 Apr, 2025 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-6539217","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":472181169,"identity":"c23a62e2-9d9e-4532-9dd6-54d6bf7a13f6","order_by":0,"name":"Yufeng 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With this intentional ambiguity and complexity, sarcasm detection has always been a challenging task, even for humans. The importance of sarcasm detection has been realized by industry and academia. However, existing methodologies for automatic sarcasm detection predominantly rely on lexical and linguistic indicators, which prove to be insufficiently effective in addressing the complexities of sarcasm recognition.This paper focuses on tackling \u0026nbsp;the challenging problem of sarcasm detection by leveraging a novel Contextual-Representation Multihop-Attention (CRMA) network based on bidirectional GRU. CRMA not only understands the ambiguity of sarcasm by the combination of contextual representation and word representation, but also learns the semantic complexity among sentences by the multihop attention mechanism. 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