Opinion-Tree-aware Prompt Tuning for Aspect Sentiment Quadruple Prediction

Opinion-Tree-aware Prompt Tuning for Aspect Sentiment Quadruple Prediction | 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 Opinion-Tree-aware Prompt Tuning for Aspect Sentiment Quadruple Prediction Zhibo Zhang, Zhenyu Yang, Zhijun Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4011287/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract In recent years, Aspect Sentiment Quadruple Prediction (ASQP) has emerged as a prominent task in the field of aspect-based sentiment analysis. We observe complex dependencies among aspect sentiment elements, rendering traditional sequence-to-sequence (Seq2Seq) modeling for element extraction insufficiently rigorous. Additionally, despite the widespread application of Pre-trained Language Models (PLMs) in Aspect-Based Sentiment Analysis (ABSA), there is a significant gap when representing dependencies between sentiment elements, which involve complex structure-informed label classification, diverging from PLM's Masked Language Model (MLM) pre-training tasks. This gap limits the full potential of PLMs. To bridge this gap and better adapt to the structural information between sentiment elements, we propose a Opinion-Tree-aware Prompt Tuning (OTPT) method. This approach transforms traditional PLM Seq2Seq tasks into sequence-to-tree tasks, optimizing PLM potential. Specifically, we model sentiment elements as a tree structure, creating a dynamic virtual template and label words, integrating tree structure information as soft prompts, and conducting stepwise sentiment label classification. Extensive experiments show that our method achieves performance superior to baseline on two widely used datasets, demonstrating good robustness in handling cases of missing sentiment elements and complex multi-sentiment quadruples. ABSA ASQP NLP deep learning Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions 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-4011287","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":276824205,"identity":"228b4d91-d5bf-4e5e-8244-3b9e6d528a43","order_by":0,"name":"Zhibo Zhang","email":"","orcid":"","institution":"Qilu University of Technology (Shandong Academy of Sciences)","correspondingAuthor":false,"prefix":"","firstName":"Zhibo","middleName":"","lastName":"Zhang","suffix":""},{"id":276824202,"identity":"f1fdf8cb-f999-4090-a28b-8efd889dec10","order_by":1,"name":"Zhenyu Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYDACdhhDAkza8PDzNxDQwoyqJU1GcsYB0rQctjFoSMCvg+8wjwEzT80du/mzm4895m07z2PAcIDxw8cc3FokwVqOPUtunHMs3Zi37TaPOXMDs+TMbbi1GIC1sB1OZpbIMZMGabFsOMDGzEtQy7/DyWwS+d+AWs7xGBxIIEILb9thOx6JHDaglgOEtUgeZitgnNt3OEFCIs1Mcs65ZB7JGQeb8fqF73jzBoY33w7by89IfibxpszOnp+/+eCHj3i0MBxgYP/Fw8CQ2ABkM/GAhRgb8KgHa2Fg/MHAYA9W+wO/2lEwCkbBKBihAADWNEyW+GsJdgAAAABJRU5ErkJggg==","orcid":"","institution":"Qilu University of Technology (Shandong Academy of Sciences)","correspondingAuthor":true,"prefix":"","firstName":"Zhenyu","middleName":"","lastName":"Yang","suffix":""},{"id":276824203,"identity":"37a51b2c-75ed-415b-96b3-292cd8c39642","order_by":2,"name":"Zhijun Li","email":"","orcid":"","institution":"Qilu University of Technology (Shandong Academy of Sciences)","correspondingAuthor":false,"prefix":"","firstName":"Zhijun","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-03-04 09:34:28","currentVersionCode":2,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4011287/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-4011287/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64196543,"identity":"4cda0e4c-1a67-4c60-88eb-a3473109e8be","added_by":"auto","created_at":"2024-09-09 21:21:34","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":550394,"visible":true,"origin":"","legend":"","description":"","filename":"OpinionTreeawarePromptTuningforAspectSentimentQuadruplePrediction.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4011287/v2_covered_d8db4d9d-530c-4d35-a687-f733c3a133d7.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"Opinion-Tree-aware Prompt Tuning for Aspect Sentiment Quadruple Prediction","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"ABSA, ASQP, NLP, deep learning","lastPublishedDoi":"10.21203/rs.3.rs-4011287/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4011287/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In recent years, Aspect Sentiment Quadruple Prediction (ASQP) has emerged as a prominent task in the field of aspect-based sentiment analysis. We observe complex dependencies among aspect sentiment elements, rendering traditional sequence-to-sequence (Seq2Seq) modeling for element extraction insufficiently rigorous. Additionally, despite the widespread application of Pre-trained Language Models (PLMs) in Aspect-Based Sentiment Analysis (ABSA), there is a significant gap when representing dependencies between sentiment elements, which involve complex structure-informed label classification, diverging from PLM's Masked Language Model (MLM) pre-training tasks. This gap limits the full potential of PLMs. To bridge this gap and better adapt to the structural information between sentiment elements, we propose a Opinion-Tree-aware Prompt Tuning (OTPT) method. This approach transforms traditional PLM Seq2Seq tasks into sequence-to-tree tasks, optimizing PLM potential. 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