Data-efficient playlist captioning with musical and linguistic knowledge

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dc.contributor.authorGabbolini, Giovanni
dc.contributor.authorHennequin, Romain
dc.contributor.authorEpure, Elena
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2023-01-17T10:10:37Z
dc.date.available2023-01-17T10:10:37Z
dc.date.issued2022-12-07
dc.description.abstractMusic streaming services feature billions of playlists created by users, professional editors or algorithms. In this content overload scenario, it is crucial to characterise playlists, so that music can be effectively organised and accessed. Playlist titles and descriptions are proposed in natural language either manually by music editors and users or automatically from pre-defined templates. However, the former is time-consuming while the latter is limited by the vocabulary and covered music themes. In this work, we propose PLAYNTELL, a data-efficient multi-modal encoder-decoder model for automatic playlist captioning. Compared to existing music captioning algorithms, PLAYN TELL leverages also linguistic and musical knowledge to generate correct and thematic captions. We benchmark PLAYNTELL on a new editorial playlists dataset collected from two major music streaming services. PLAYNTELL yields 2x-3x higher BLEU@4 and CIDEr than state of the art captioning algorithmsen
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationGabbolini, G., Hennequin, R. and Epure, E. (2022) ‘Data-Efficient Playlist Captioning With Musical and Linguistic Knowledge’, EMNLP 2022, Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Abu Dhabi, UAE, 7-11 Dec., Association for Computational Linguistics, pp. 11401-11415.en
dc.identifier.endpage11415en
dc.identifier.startpage11401en
dc.identifier.urihttps://hdl.handle.net/10468/14062
dc.language.isoenen
dc.publisherAssociation for Computational Linguisticsen
dc.relation.ispartofProceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/en
dc.relation.urihttps://preview.aclanthology.org/emnlp-22-ingestion/2022.emnlp-main.784/
dc.rights© 2022en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectPlaylistsen
dc.subjectMusic streamingen
dc.subjectPlaylist captioningen
dc.subjectRecommender systemsen
dc.subjectCaptioning algorithmsen
dc.titleData-efficient playlist captioning with musical and linguistic knowledgeen
dc.typeConference itemen
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