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Oubli Catastrophique et Modèles Neuronaux de Recherche d'Information

Abstract : In this paper, we study in what extent neural ranking models catastrophically forget old knowledge acquired from previously observed domains after acquiring new knowledge (i.e.on new datasets), leading to performance decrease on those domains. Our experiments carried out on 4 datastes and 5 neural IR ranking models show that catastrophic forgetting is prevalentand that a lifelong learning strategy mitigates the problem. Using an explanatory approach builton a regression model, we also identify domain characteristics that can predict catastrophic for getting .Note: this paper is a summary of the ECIR 2021 paper (Lovón-Melgarejoet al., 2021).
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Contributor : Laure Soulier Connect in order to contact the contributor
Submitted on : Friday, July 30, 2021 - 12:06:14 PM
Last modification on : Monday, July 4, 2022 - 9:44:37 AM
Long-term archiving on: : Sunday, October 31, 2021 - 6:14:54 PM


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  • HAL Id : hal-03309985, version 1


Jesus Lovon Melgarejo, Laure Soulier, Karen Pinel-Sauvagnat, Lynda Tamine. Oubli Catastrophique et Modèles Neuronaux de Recherche d'Information. 17ème conférence francophone en Recherche d’Information et Application (CORIA 2021), ARIA : Association Francophone de Recherche d’Information (RI) et Applications; Equipe MRIM du Laboratoire d’Informatique de Grenoble., Apr 2021, Grenoble (virtuel), France. pp.1-5. ⟨hal-03309985⟩



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