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Interpretable emoji prediction via label-wise attention LSTMs

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dc.contributor.author Barbieri, Francesco
dc.contributor.author Espinosa-Anke, Luis
dc.contributor.author Camacho-Collados, Jose
dc.contributor.author Schockaert, Steven
dc.contributor.author Saggion, Horacio
dc.date.accessioned 2018-12-03T10:51:43Z
dc.date.available 2018-12-03T10:51:43Z
dc.date.issued 2018
dc.identifier.citation Barbieri F, Espinosa-Anke L, Camacho-Collados J, Schockaert S, Saggion H. Interpretable emoji prediction via label-wise attention LSTMs. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing; 2018 Oct 31-Nov 4; Brussels, Belgium. New York: Association for Computational Linguistics; 2018. p. 4766-71.
dc.identifier.isbn 978-1-948087-84-1
dc.identifier.issn 1530-9312
dc.identifier.uri http://hdl.handle.net/10230/35940
dc.description Comunicació presentada a la Conference on Empirical Methods in Natural Language Processing, celebrada del 31 d'octubre al 4 de novembre de 2018 a Brussel·les, Bèlgica.
dc.description.abstract Human language has evolved towards newer forms of communication such as social media, where emojis (i.e., ideograms bearing a visual meaning) play a key role. While there is an increasing body of work aimed at the computational modeling of emoji semantics, there is currently little understanding about what makes a computational model represent or predict a given emoji in a certain way. In this paper we propose a label-wise attention mechanism with which we attempt to better understand the nuances underlying emoji prediction. In addition to advantages in terms of interpretability, we show that our proposed architecture improves over standard baselines in emoji prediction, and does particularly well when predicting infrequent emojis.
dc.description.sponsorship F. Barbieri and H. Saggion acknowledge support from the TUNER project (TIN2015-65308-C5-5- R, MINECO/FEDER, UE). Luis Espinosa-Anke, Jose Camacho-Collados and Steven Schockaert have been supported by ERC Starting Grant 637277.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher ACL (Association for Computational Linguistics)
dc.relation.ispartof Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing; 2018 Oct 31-Nov 4; Brussels, Belgium. New York: Association for Computational Linguistics; 2018.
dc.rights © ACL, Creative Commons Attribution 4.0 License
dc.title Interpretable emoji prediction via label-wise attention LSTMs
dc.type info:eu-repo/semantics/conferenceObject
dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/TIN2015-65308-C5-5-R
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion

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