dc.contributor.author | Kelleher, John | |
dc.date.accessioned | 2022-03-21T10:53:43Z | |
dc.date.available | 2022-03-21T10:53:43Z | |
dc.date.issued | 2020 | |
dc.date.submitted | 2020 | en |
dc.identifier.citation | Trinh, 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, 798-810 | en |
dc.identifier.other | Y | |
dc.identifier.uri | http://hdl.handle.net/2262/98319 | |
dc.description.abstract | In 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.extent | 798-810 | en |
dc.language.iso | en | en |
dc.relation.ispartofseries | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); | |
dc.relation.ispartofseries | 12397 LNCS; | |
dc.rights | Y | en |
dc.subject | dialogue slot-value constraint rules | en |
dc.subject | multi-label classification methods | en |
dc.subject | real conversation scenarios | en |
dc.subject | Dialogue processing | en |
dc.subject | Multi-label classification | en |
dc.subject | Label regularisation | en |
dc.subject | F-measure optimisation | en |
dc.subject | Energy-based learning | en |
dc.subject | Neural dialogue state tracking | en |
dc.title | F-Measure Optimisation and Label Regularisation for Energy-Based Neural Dialogue State Tracking Models | en |
dc.type | Journal Article | en |
dc.type.supercollection | scholarly_publications | en |
dc.type.supercollection | refereed_publications | en |
dc.identifier.peoplefinderurl | http://people.tcd.ie/kellehjd | |
dc.identifier.rssinternalid | 224460 | |
dc.identifier.doi | http://dx.doi.org/10.1007/978-3-030-61616-8_64 | |
dc.rights.ecaccessrights | openAccess | |
dc.contributor.sponsor | Science Foundation Ireland (SFI) | en |