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dc.contributor.authorKelleher, John
dc.date.accessioned2022-03-21T10:53:43Z
dc.date.available2022-03-21T10:53:43Z
dc.date.issued2020
dc.date.submitted2020en
dc.identifier.citationTrinh, A.D. and Ross, R.J. and Kelleher, J.D., F-Measure Optimisation and Label Regularisation for Energy-Based Neural Dialogue State Tracking Models, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12397 LNCS, 2020, 798-810en
dc.identifier.otherY
dc.identifier.urihttp://hdl.handle.net/2262/98319
dc.description.abstractIn recent years many multi-label classification methods have exploited label dependencies to improve performance of classification tasks in various domains, hence casting the tasks to structured prediction problems. We argue that multi-label predictions do not always satisfy domain constraint restrictions. For example when the dialogue state tracking task in task-oriented dialogue domains is solved with multi-label classification approaches, slot-value constraint rules should be enforced following real conversation scenarios. To address these issues we propose an energy-based neural model to solve the dialogue state tracking task as a structured prediction problem. Furthermore we propose two improvements over previous methods with respect to dialogue slot-value constraint rules: (i) redefining the estimation conditions for the energy network; (ii) regularising label predictions following the dialogue slot-value constraint rules. In our results we find that our extended energy-based neural dialogue state tracker yields better overall performance in term of prediction accuracy, and also behaves more naturally with respect to the conversational rules.en
dc.format.extent798-810en
dc.language.isoenen
dc.relation.ispartofseriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics);
dc.relation.ispartofseries12397 LNCS;
dc.rightsYen
dc.subjectdialogue slot-value constraint rulesen
dc.subjectmulti-label classification methodsen
dc.subjectreal conversation scenariosen
dc.subjectDialogue processingen
dc.subjectMulti-label classificationen
dc.subjectLabel regularisationen
dc.subjectF-measure optimisationen
dc.subjectEnergy-based learningen
dc.subjectNeural dialogue state trackingen
dc.titleF-Measure Optimisation and Label Regularisation for Energy-Based Neural Dialogue State Tracking Modelsen
dc.typeJournal Articleen
dc.contributor.sponsorScience Foundation Ireland (SFI)en
dc.type.supercollectionscholarly_publicationsen
dc.type.supercollectionrefereed_publicationsen
dc.identifier.peoplefinderurlhttp://people.tcd.ie/kellehjd
dc.identifier.rssinternalid224460
dc.identifier.doihttp://dx.doi.org/10.1007/978-3-030-61616-8_64
dc.rights.ecaccessrightsopenAccess


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