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CNNs found to jump around more skillfully than RNNs: compositional generalization in seq2seq convolutional networks

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dc.contributor.author Dessi, Roberto
dc.contributor.author Baroni, Marco
dc.date.accessioned 2022-12-02T07:07:27Z
dc.date.available 2022-12-02T07:07:27Z
dc.date.issued 2019
dc.identifier.citation Dessi R, Baroni M. CNNs found to jump around more skillfully than RNNs: compositional generalization in seq2seq convolutional networks. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019; 2019 Jul 28 - Aug 2; Florence, Italy. Stroudsburg: Association for Computational Linguistics; 2019. p. 3919-23.
dc.identifier.uri http://hdl.handle.net/10230/55068
dc.description Comunicació presentada a la 57th Annual Meeting of the Association for Computational Linguistics, celebrada del 28 de juliol al 2 d'agost de 2019 a Florència, Itàlia
dc.description.abstract Lake and Baroni (2018) introduced the SCAN dataset probing the ability of seq2seq models to capture compositional generalizations, such as inferring the meaning of “jump around” 0- shot from the component words. Recurrent networks (RNNs) were found to completely fail the most challenging generalization cases. We test here a convolutional network (CNN) on these tasks, reporting hugely improved performance with respect to RNNs. Despite the big improvement, the CNN has however not induced systematic rules, suggesting that the difference between compositional and noncompositional behaviour is not clear-cut.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher ACL (Association for Computational Linguistics)
dc.relation.ispartof Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019; 2019 Jul 28 - Aug 2; Florence, Italy. Stroudsburg: Association for Computational Linguistics; 2019. p. 3919-23
dc.rights © ACL, Creative Commons Attribution 4.0 License
dc.rights.uri https://creativecommons.org/licenses/by/4.0/
dc.subject Lingüística computacional
dc.subject Intel·ligència artificial
dc.subject Aprenentatge automàtic
dc.title CNNs found to jump around more skillfully than RNNs: compositional generalization in seq2seq convolutional networks
dc.type info:eu-repo/semantics/conferenceObject
dc.identifier.doi https://doi.org/10.18653/v1/P19-1381
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion

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