Multi-way Variational NMT for UGC: Improving Robustness in Zero-shot Scenarios via Mixture Density Networks

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dc.contributor.authorNúñez, José Carlos Rosales
dc.contributor.authorSeddah, Djamé
dc.contributor.authorWisniewski, Guillaume
dc.coverage.temporalMay 22-24, 2023
dc.date.accessioned2023-05-19T08:59:28Z
dc.date.available2023-05-19T08:59:28Z
dc.date.issued2023-05
dc.identifier.issnISSN 1736-6305 (Online)
dc.identifier.urihttp://hdl.handle.net/10062/89904
dc.language.isoenget
dc.publisherUniversity of Tartu Libraryet
dc.relation.ispartofNEALT Proceedings Series, No. 52
dc.relation.ispartofseriesProceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pages 447–459;
dc.rightsopenAccesset
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subject.otherNoDaLiDa 2023et
dc.titleMulti-way Variational NMT for UGC: Improving Robustness in Zero-shot Scenarios via Mixture Density Networkset
dc.typeArticleet

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