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How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models
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Phillip Rust, Jonas Pfeiffer, Ivan Vulić, Sebastian Ruder, Iryna Gurevych. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.
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“increasing inference cost and compressing effective context windows (Rust et al., 2021; Petrov et al., 2024).”
From Writing-System-Level Tokenizer Adaptation for Byte-Level BPE (2026) · cites this paper as Rust et al., 2021
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Metadata
- Authors
- Rust, P., Pfeiffer, J., Vulić, I., Ruder, S., & Gurevych, I.
- Affiliations
- Technische Universität Darmstadt (DE)
- University of Cambridge (GB)
- Venue
- Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
- Published
- 2021
- Pages
- 3118-3135
- OpenAlex
- W3120253119
- Cited by
- 202
- References
- 57
- Citations by year
- 2026: 32 · 2025: 32 · 2024: 22 · 2023: 50 · 2022: 35 · 2021: 31
- FWCI
- 15.94
- Type
- conference paper
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- arXiv (Cornell University) (submittedVersion)
- TUbilio (Technical University of Darmstadt) (submittedVersion, cc-by-nc-sa)
- arXiv (Cornell University)
- Funders
- H2020 European Research Council (648909)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Keywords
- tokenization, multilingual language models, multilingual tokenization, language model evaluation
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