| Téléchargement | - Voir la version finale : Text style transfer: leveraging a style classifier on entangled latent representations (PDF, 968 Kio)
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| DOI | Trouver le DOI : https://doi.org/10.18653/v1/2021.repl4nlp-1.9 |
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| Auteur | Rechercher : Li, Xiaoyan; Rechercher : Sun, Sun1; Rechercher : Wang, Yunli1 |
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| Affiliation | - Conseil national de recherches du Canada. Technologies numériques
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| Format | Texte, Article |
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| Conférence | 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), Aug. 6th, 2021, Bangkok, Thailand (Online) |
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| Résumé | Learning a good latent representation is essential for text style transfer, which generates a new sentence by changing the attributes of a given sentence while preserving its content. Most previous works adopt disentangled latent representation learning to realize style transfer. We propose a novel text style transfer algorithm with entangled latent representation, and introduce a style classifier that can regulate the latent structure and transfer style. Moreover, our algorithm for style transfer applies to both single-attribute and multi-attribute transfer. Extensive experimental results show that our method generally outperforms state-of-the-art approaches. |
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| Date de publication | 2021-08-06 |
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| Maison d’édition | Association for Computational Linguistics |
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| Licence | |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | a6888c23-c792-4543-9ca0-43fa53abc356 |
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| Enregistrement créé | 2021-09-10 |
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| Enregistrement modifié | 2021-09-14 |
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