| Téléchargement | - Voir la version finale : N-gram and neural models for Uralic language identification: NRC at VarDial 2021 (PDF, 377 Kio)
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| Auteur | Rechercher : Bernier-Colborne, Gabriel1; Rechercher : Léger, Serge1; Rechercher : Goutte, Cyril1 |
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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 | 8th VarDial Workshop on NLP for Similar Languages, Varieties and Dialects, April 20th, 2021, Held Virtually |
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| Résumé | We describe the systems developed by the National Research Council Canada for the Uralic language identification shared task at the 2021 VarDial evaluation campaign. We evaluated two different approaches to this task: a probabilistic classifier exploiting only character 5-grams as features, and a character-based neural network pre-trained through self-supervision, then fine-tuned on the language identification task. The former method turned out to perform better, which casts doubt on the usefulness of deep learning methods for language identification, where they have yet to convincingly and consistently outperform simpler and less costly classification algorithms exploiting n-gram features. |
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| Date de publication | 2021-04-20 |
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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 | ff9f3cc0-73cd-4026-bcf7-3b60e247330d |
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| Enregistrement créé | 2021-08-03 |
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| Enregistrement modifié | 2021-08-04 |
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