A Joint Model of Multiple Intent Recognition and Slot Filling Based on Graph Neural Network

A Joint Model of Multiple Intent Recognition and Slot Filling Based on Graph Neural 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 Research Article A Joint Model of Multiple Intent Recognition and Slot Filling Based on Graph Neural Network Jinjie Huang, Huashuai Tang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4117989/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 Intention recognition and slot filling tasks are two important tasks in oral comprehension, and they are closely related. The study of joint models for both tasks is a current research hotspot. However, most current joint models are based on a single information flow model, only considering the impact of intention on slot information and ignoring the information flow from slot to intention. In response to this issue, this article proposes a joint model based on graph neural networks for multi-intention recognition and slot filling to achieve bidirectional information flow modeling. Among them, BERT is used as a shared encoder to improve the quality of semantic feature extraction. An adaptive graph convolutional network is proposed to extract slot information in intention recognition tasks, facilitating information exchange from slot to intention. In the slot filling task, feature fusion and a graph attention mechanism are employed to aggregate intention information, achieving information flow from intention to slot. Through experimental verification, the bidirectional information exchange effect is evident. Compared with the current GL-GIN model, the total accuracy of the proposed model has been improved by 1.7% and 2.1% on two common datasets, respectively. Graph neural network intentional identification slot filling joint model 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. 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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-4117989","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":282278428,"identity":"f475d4ef-9fa3-459f-ac1e-3d0277080c5c","order_by":0,"name":"Jinjie Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYBACfv7GBgMJAwlmxvbmA8RpkZxxuKHAosCGnbnnWAJxWgwOpDd8qPiQxs8+I8eASJcdONi44YbBYWneGTkfb7xhsJPTbSCgg7G5sdlwhsFhY8met5st5zAkG5sdIKCFmeFgm7GEweFkw/bcbdI8DAcStxHSwsaQ2P77j8Hh+v0Hcp4Rp4WHIREUyGnMjB05bMRpkZA4CNJiw8zYc8zYco4BEX6xP9/+wEDiDzgqH954U2EnR1ALqpU8xEYNkhZSdYyCUTAKRsGIAAA82UbbyTx4fwAAAABJRU5ErkJggg==","orcid":"","institution":"Harbin University of Science and Technology","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Jinjie","middleName":"","lastName":"Huang","suffix":""},{"id":282278429,"identity":"878d316e-a4a1-430a-9021-e40ead019ab4","order_by":1,"name":"Huashuai Tang","email":"","orcid":"","institution":"Harbin University of Science and Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Huashuai","middleName":"","lastName":"Tang","suffix":""}],"badges":[],"createdAt":"2024-03-17 16:44:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4117989/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4117989/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53316611,"identity":"6fc715fe-6d95-4016-8e09-a8c31a2b3682","added_by":"auto","created_at":"2024-03-23 19:22:34","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":300465,"visible":true,"origin":"","legend":"","description":"","filename":"AJointModelofMultipleIntentRecognitionandSlotFillingBasedonGraphNeuralNetwork.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4117989/v1_covered_8ab9af08-d150-4628-89d8-64a803ba4245.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Joint Model of Multiple Intent Recognition and Slot Filling Based on Graph Neural Network","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"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":"Graph neural network, intentional identification, slot filling, joint model","lastPublishedDoi":"10.21203/rs.3.rs-4117989/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4117989/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIntention recognition and slot filling tasks are two important tasks in oral comprehension, and they are closely related. The study of joint models for both tasks is a current research hotspot. However, most current joint models are based on a single information flow model, only considering the impact of intention on slot information and ignoring the information flow from slot to intention. In response to this issue, this article proposes a joint model based on graph neural networks for multi-intention recognition and slot filling to achieve bidirectional information flow modeling. Among them, BERT is used as a shared encoder to improve the quality of semantic feature extraction. An adaptive graph convolutional network is proposed to extract slot information in intention recognition tasks, facilitating information exchange from slot to intention. In the slot filling task, feature fusion and a graph attention mechanism are employed to aggregate intention information, achieving information flow from intention to slot. 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