| Téléchargement | - Voir la version finale : Improving cuneiform language identification with BERT (PDF, 292 Kio)
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| DOI | Trouver le DOI : https://doi.org/10.18653/v1/W19-1402 |
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| Auteur | Rechercher : Bernier-Colborne, Gabriel1; Rechercher : Goutte, Cyril1; Rechercher : Léger, Serge1 |
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| Affiliation | - Conseil national de recherches Canada. Technologies numériques
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| Format | Texte, Article |
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| Conférence | The Sixth Workshop on NLP for Similar Languages, Varieties and Dialects, 2019-6 - 2019-6, Ann Arbor, MI, USA |
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| Résumé | We describe the systems developed by the National Research Council Canada for the Cuneiform Language Identification (CLI) shared task at the 2019 VarDial evaluation campaign. We compare a state-of-the-art baseline relying on character n-grams and a traditional statistical classifier, a voting ensemble of classifiers, and a deep learning approach using a Transformer network. We describe how these systems were trained, and analyze the impact of some preprocessing and model estimation decisions. The deep neural network achieved 77% accuracy on the test data, which turned out to be the best performance at the CLI evaluation, establishing a new state-of-the-art for cuneiform language identification. |
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| Date de publication | 2019 |
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| Maison d’édition | Association for Computational Linguistics |
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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 | e6334b65-1734-4c0e-86da-7fb23d5cf6be |
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| Enregistrement créé | 2019-12-19 |
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| Enregistrement modifié | 2020-05-30 |
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