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dc.contributor.authorLehečka, Jan
dc.contributor.authorŠvec, Jan
dc.contributor.authorIrcing, Pavel
dc.contributor.authorŠmídl, Luboš
dc.date.accessioned2021-03-01T11:00:24Z-
dc.date.available2021-03-01T11:00:24Z-
dc.date.issued2020
dc.identifier.citationLEHEČKA, J., ŠVEC, J., IRCING, P., ŠMÍDL, L. BERT-Based Sentiment Analysis Using Distillation. In Statistical Language and Speech Processing, SLSP 2020. Cham: Springer, 2020. s. 58-70. ISBN 978-3-030-59429-9, ISSN 0302-9743.cs
dc.identifier.isbn978-3-030-59429-9
dc.identifier.issn0302-9743
dc.identifier.uri2-s2.0-85092196103
dc.identifier.urihttp://hdl.handle.net/11025/42765
dc.format13 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherSpringeren
dc.relation.ispartofseriesStatistical Language and Speech Processing, SLSP 2020en
dc.rightsPlný text je přístupný v rámci univerzity přihlášeným uživatelům.cs
dc.rights© Springeren
dc.titleBERT-Based Sentiment Analysis Using Distillationen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.rights.accessrestrictedAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedIn this paper, we present our experiments with BERT (Bidirectional Encoder Representations from Transformers) models in the task of sentiment analysis, which aims to predict the sentiment polarity for the given text. We trained an ensemble of BERT models from a large self-collected movie reviews dataset and distilled the knowledge into a single production model. Moreover, we proposed an improved BERT’s pooling layer architecture, which outperforms standard classification layer while enables per-token sentiment predictions. We demonstrate our improvements on a publicly available dataset with Czech movie reviews.en
dc.subject.translatedSentiment analysisen
dc.subject.translatedBERTen
dc.subject.translatedKnowledge distillationen
dc.identifier.doi10.1007/978-3-030-59430-5_5
dc.type.statusPeer-revieweden
dc.identifier.obd43930643
dc.project.IDTN01000024/Národní centrum kompetence - Kybernetika a umělá inteligencecs
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Konferenční příspěvky / Conference Papers (KKY)
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Please use this identifier to cite or link to this item: http://hdl.handle.net/11025/42765

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