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Does referent predictability affect the choice of referential form? A computational approach using masked coreference resolution

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dc.contributor.author Aina, Laura
dc.contributor.author Liao, Xixian
dc.contributor.author Boleda, Gemma
dc.contributor.author Westera, Matthijs
dc.date.accessioned 2023-04-03T06:14:58Z
dc.date.available 2023-04-03T06:14:58Z
dc.date.issued 2021
dc.identifier.citation Aina L, Liao X, Boleda G, Westera M. Does referent predictability affect the choice of referential form? A computational approach using masked coreference resolution. In: Bisazza A, Abend O, editors. Proceedings of the 25th Conference on Computational Natural Language Learning (CoNLL 2021); 2021 Nov 10-11; [online]. [Stroudsburg]: Association for Computational Linguistics; 2021. p. 454–69. DOI: 10.18653/v1/2021.conll-1.36
dc.identifier.uri http://hdl.handle.net/10230/56393
dc.description Comunicació presentada a 25th Conference on Computational Natural Language Learning (CoNLL 2021) celebrada en línia del 10 al 11 de 2021
dc.description.abstract It is often posited that more predictable parts of a speaker’s meaning tend to be made less explicit, for instance using shorter, less informative words. Studying these dynamics in the domain of referring expressions has proven difficult, with existing studies, both psycholinguistic and corpus-based, providing contradictory results. We test the hypothesis that speakers produce less informative referring expressions (e.g., pronouns vs. full noun phrases) when the context is more informative about the referent, using novel computational estimates of referent predictability. We obtain these estimates training an existing coreference resolution system for English on a new task, masked coreference resolution, giving us a probability distribution over referents that is conditioned on the context but not the referring expression. The resulting system retains standard coreference resolution performance while yielding a better estimate of human-derived referent predictability than previous attempts. A statistical analysis of the relationship between model output and mention form supports the hypothesis that predictability affects the form of a mention, both its morphosyntactic type and its length.
dc.description.sponsorship This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 715154).
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher ACL (Association for Computational Linguistics)
dc.relation.ispartof Bisazza A, Abend O, editors. Proceedings of the 25th Conference on Computational Natural Language Learning (CoNLL 2021); 2021 Nov 10-11; [online]. [Stroudsburg]: Association for Computational Linguistics; 2021. p. 454–69.
dc.rights © ACL, Creative Commons Attribution 4.0 License
dc.rights.uri https://creativecommons.org/licenses/by/4.0/
dc.subject.other Lingüística computacional
dc.subject.other Formació de mots
dc.subject.other Gramàtica comparada i general
dc.subject.other Morfologia
dc.title Does referent predictability affect the choice of referential form? A computational approach using masked coreference resolution
dc.type info:eu-repo/semantics/conferenceObject
dc.identifier.doi http://dx.doi.org/10.18653/v1/2021.conll-1.36
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/715154
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion

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