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dc.contributor.authorBiesialska, Magdalena Marta
dc.contributor.authorBiesialska, Katarzyna
dc.contributor.authorRuiz Costa-Jussà, Marta
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Teoria del Senyal i Comunicacions
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Computació
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.identifier.citationBiesialska, M.; Biesialska, K.; Costa-jussà, M.R. Continual lifelong learning in natural language processing: a survey. A: International Conference on Computational Linguistics. "COLING 2020, The 28th International Conference on Computational Linguistics: December 8-13, 2020, Barcelona, Spain (online): proceedings of the conference". Stroudsburg, PA: Association for Computational Linguistics, 2020, p. 6523-6541. ISBN 978-1-952148-27-9. DOI 10.18653/v1/2020.coling-main.574.
dc.description.abstractContinual learning (CL) aims to enable information systems to learn from a continuous data stream across time. However, it is difficult for existing deep learning architectures to learn a new task without largely forgetting previously acquired knowledge. Furthermore, CL is particularly challenging for language learning, as natural language is ambiguous: it is discrete, compositional, and its meaning is context-dependent. In this work, we look at the problem of CL through the lens of various NLP tasks. Our survey discusses major challenges in CL and current methods applied in neural network models. We also provide a critical review of the existing CL evaluation methods and datasets in NLP. Finally, we present our outlook on future research directions.
dc.description.sponsorshipThis work is supported in part by the Catalan Agencia de Gestión de Ayudas Universitarias y de Investigación (AGAUR) through the FI PhD grant; the Spanish Ministerio de Ciencia e Innovación and by the Agencia Estatal de Investigación through the Ramón y Cajal grant and the project PCIN-2017-079; and by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 947657).
dc.format.extent19 p.
dc.publisherAssociation for Computational Linguistics
dc.rightsAttribution 4.0 International
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Llenguatge natural
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
dc.subject.lcshMachine learning
dc.subject.lcshNatural language processing (Computer science)
dc.subject.otherContinual learning
dc.subject.otherDeep learning
dc.subject.otherNeural networks
dc.titleContinual lifelong learning in natural language processing: a survey
dc.typeConference lecture
dc.subject.lemacAprenentatge automàtic
dc.subject.lemacTractament del llenguatge natural (Informàtica)
dc.contributor.groupUniversitat Politècnica de Catalunya. inSSIDE - integrated Software, Service, Information and Data Engineering
dc.contributor.groupUniversitat Politècnica de Catalunya. VEU - Grup de Tractament de la Parla
dc.description.peerreviewedPeer Reviewed
dc.rights.accessOpen Access
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/947657/EU/Lifelong UNiversal lAnguage Representation/LUNAR
local.citation.authorBiesialska, M.; Biesialska, K.; Costa-jussà, M. R.
local.citation.contributorInternational Conference on Computational Linguistics
local.citation.pubplaceStroudsburg, PA
local.citation.publicationNameCOLING 2020, The 28th International Conference on Computational Linguistics: December 8-13, 2020, Barcelona, Spain (online): proceedings of the conference

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