| Download | - View final version: N-gram and neural models for Uralic language identification: NRC at VarDial 2021 (PDF, 377 KiB)
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| Author | Search for: Bernier-Colborne, Gabriel1; Search for: Léger, Serge1; Search for: Goutte, Cyril1 |
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| Affiliation | - National Research Council of Canada. Digital Technologies
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| Format | Text, Article |
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| Conference | 8th VarDial Workshop on NLP for Similar Languages, Varieties and Dialects, April 20th, 2021, Held Virtually |
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| Abstract | 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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| Publication date | 2021-04-20 |
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| Publisher | Association for Computational Linguistics |
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| Licence | |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | ff9f3cc0-73cd-4026-bcf7-3b60e247330d |
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| Record created | 2021-08-03 |
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| Record modified | 2021-08-04 |
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