Who cites «On the Importance of Tokenization in Arabic Embedding Models»

11 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. ← the card

1. A Comprehensive Survey of Contemporary Arabic Sentiment Analysis: Methods, Challenges, and Future Directions — 2025 · Findings of the Association for Computational Linguistics: NAACL 2025
2. Automated Authentication of Quranic Verses Using BERT (Bidirectional Encoder Representations from Transformers) based Language Models — 2025 · Proceedings of the New Horizons in Computational Linguistics for Religious Texts
3. Beyond Literal Token Overlap: Token Alignability for Multilinguality — 2025 · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)
4. DeformAr: Rethinking NER Evaluation through Component Analysis and Visual Analytics — 2025 · arXiv:2512.00938
5. Semitic Root Encoding: Tokenization Based on the Templatic Morphology of Semitic Languages in NMT — our card · 2025 · Proceedings of The Third Arabic Natural Language Processing Conference
6. The Effect of Model Capacity and Script Diversity on Subword Tokenization for Sorani Kurdish — 2024 · Proceedings of the 21st SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology
7. Explicit Morphological Knowledge Improves Pre-training of Language Models for Hebrew — our card · 2023 · arXiv:2311.00658
8. Investigating Zero-shot Cross-lingual Language Understanding for Arabic — 2023 · Proceedings of ArabicNLP 2023
9. An Embarrassingly Simple Method to Mitigate Undesirable Properties of Pretrained Language Model Tokenizers — 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
10. Evaluating Various Tokenizers for Arabic Text Classification — 2021 · arXiv:2106.07540
11. You should evaluate your language model on marginal likelihood over tokenisations — 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing