--- title: "Who cites Character-Level Language — 85 citing papers" description: "85 papers of the Elamur Research library cite «Character-Level Language Modeling with Deeper Self-Attention», found in their bibliographies…" canonical: "https://research.elamur.ai/free/en/papers/al-rfou-2018-character-level-language-modeling-deeper/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/al-rfou-2018-character-level-language-modeling-deeper/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Character-Level Language Modeling with Deeper…](https://md.research.elamur.ai/free/en/papers/al-rfou-2018-character-level-language-modeling-deeper/) / cited by # Who cites «Character-Level Language Modeling with Deeper Self-Attention» 85 papers of our library cite it (of the 149,439 whose bibliographies we read) · 24 with a page here, the most cited first · [← back to «Character-Level Language Modeling with Deeper Self-Attention»](https://md.research.elamur.ai/free/en/papers/al-rfou-2018-character-level-language-modeling-deeper/) 1. **[BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://md.research.elamur.ai/free/en/papers/devlin-2019-bert-pre-training-deep-bidirectional/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Jacob Devlin, Ming-Wei Chang, Kenton Lee, et al.*2019 · 56,209 citations* 2. **[End-to-End Object Detection with Transformers](https://md.research.elamur.ai/free/en/papers/carion-2020-end-end-object-detection-transformers/) [Lecture notes in computer science](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-computer-science/)**Nicolas Carion, Francisco Massa, Gabriel Synnaeve, et al.*2020 · 16,967 citations* 3. [**Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer arXiv:1910.10683**Colin Raffel, Noam Shazeer, Adam Roberts, et al.*2019 · 11,325 citations*](https://md.research.elamur.ai/free/en/papers/raffel-2019-exploring-limits-transfer-learning-unified/) 4. [**Longformer: The Long-Document Transformer arXiv:2004.05150**Iz Beltagy, Matthew E. Peters, Arman Cohan*2020 · 3,880 citations*](https://md.research.elamur.ai/free/en/papers/beltagy-2020-longformer-long-document-transformer/) 5. [**XLNet: Generalized Autoregressive Pretraining for Language Understanding arXiv:1906.08237**Zhilin Yang, Zihang Dai, Yiming Yang, et al.*2019 · 3,857 citations*](https://md.research.elamur.ai/free/en/papers/yang-2019-xlnet-generalized-autoregressive-pretraining-language/) 6. [**Generating Long Sequences with Sparse Transformers arXiv:1904.10509**Rewon Child, Scott Gray, Alec Radford, et al.*2019 · 967 citations*](https://md.research.elamur.ai/free/en/papers/child-2019-generating-long-sequences-sparse-transformers/) 7. [**Reformer: The Efficient Transformer arXiv:2001.04451**Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya*2020 · 829 citations*](https://md.research.elamur.ai/free/en/papers/kitaev-2020-reformer-efficient-transformer/) 8. **[Learning Deep Transformer Models for Machine Translation](https://md.research.elamur.ai/free/en/papers/wang-2019-learning-deep-transformer-models-machine/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qiang Wang, Bei Li, Tong Xiao, et al.*2019 · 810 citations* 9. **[Efficient Content-Based Sparse Attention with Routing Transformers](https://md.research.elamur.ai/free/en/papers/roy-2020-efficient-content-based-sparse-attention/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Aurko Roy, Mohammad Saffar, Ashish Vaswani, et al.*2020 · 677 citations* 10. **[Adaptive Attention Span in Transformers](https://md.research.elamur.ai/free/en/papers/sukhbaatar-2019-adaptive-attention-span-transformers/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Sainbayar Sukhbaatar, Edouard Grave, Piotr Bojanowski, et al.*2019 · 367 citations* 11. [**Compressive Transformers for Long-Range Sequence Modelling arXiv:1911.05507**Jack W. Rae, Anna Potapenko, Siddhant M. Jayakumar, et al.*2019 · 340 citations*](https://md.research.elamur.ai/free/en/papers/rae-2019-compressive-transformers-long-range-sequence/) 12. [**Structured Denoising Diffusion Models in Discrete State-Spaces arXiv:2107.03006**Jacob Austin, Daniel D. Johnson, Jonathan Ho, et al.*2021 · 284 citations*](https://md.research.elamur.ai/free/en/papers/austin-2021-structured-denoising-diffusion-models-discrete/) 13. **[Surface Form Competition: Why the Highest Probability Answer Isn't Always Right](https://md.research.elamur.ai/free/en/papers/holtzman-2021-surface-form-competition-why-highest/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ari Holtzman, Peter West, Vered Shwartz, et al.*2021 · 277 citations* 14. [**Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP arXiv:2112.10508**Sabrina J. Mielke, Zaid Alyafeai, Elizabeth Salesky, et al.*2021 · 191 citations*](https://md.research.elamur.ai/free/en/papers/mielke-2021-between-words-characters-brief-history/) 15. [**Adaptive Input Representations for Neural Language Modeling arXiv:1809.10853**Alexei Baevski, Michael Auli*2018 · 177 citations*](https://md.research.elamur.ai/free/en/papers/baevski-2018-adaptive-input-representations-neural-language/) 16. **[Efficient Nearest Neighbor Language Models](https://md.research.elamur.ai/free/en/papers/he-2021-efficient-nearest-neighbor-language-models/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Junxian He, Graham Neubig, Taylor Berg-Kirkpatrick*2021 · 130 citations* 17. [**Linear Transformers Are Secretly Fast Weight Programmers arXiv:2102.11174**Imanol Schlag, Kazuki Irie, Jurgen Schmidhuber*2021 · 111 citations*](https://md.research.elamur.ai/free/en/papers/schlag-2021-linear-transformers-secretly-fast-weight/) 18. [**Evaluating Various Tokenizers for Arabic Text Classification arXiv:2106.07540**Zaid Alyafeai, Maged S. Al-shaibani, Mustafa Ghaleb, et al.*2021 · 66 citations*](https://md.research.elamur.ai/free/en/papers/alyafeai-2021-evaluating-various-tokenizers-arabic-text/) 19. **[On the limit of English conversational speech recognition](https://md.research.elamur.ai/free/en/papers/tuske-2021-limit-english-conversational-speech-recognition/) [Interspeech](https://md.research.elamur.ai/free/en/venues/interspeech-usb/)**Zoltan Tuske, George Saon, Brian Kingsbury*2021 · 49 citations* 20. [**MambaByte: Token-free Selective State Space Model arXiv:2401.13660**Junxiong Wang, Tushaar Gangavarapu, Jing Nathan Yan, et al.*2024 · 35 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-mambabyte-token-free-selective-state/) 21. **[MAGNET: Improving the Multilingual Fairness of Language Models with Adaptive Gradient-Based Tokenization](https://md.research.elamur.ai/free/en/papers/ahia-2024-magnet-improving-multilingual-fairness-language/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Orevaoghene Ahia, Sachin Kumar, Hila Gonen, et al.*2024 · 19 citations* 22. [**MoVoC: Morphology-Aware Subword Construction for Ge'ez Script Languages**Hailay Kidu Teklehaymanot, Dren Fazlija, Wolfgang Nejdl*2025 · 4 citations*](https://md.research.elamur.ai/free/en/papers/teklehaymanot-2025-movoc-morphology-aware-subword-construction/) 23. [**MoVoC: Morphology-Aware Subword Construction for Geez Script Languages arXiv:2509.08812**Hailay Kidu Teklehaymanot, Dren Fazlija, Wolfgang Nejdl*2025 · 4 citations*](https://md.research.elamur.ai/free/en/papers/teklehaymanot-2025-movoc-morphology-aware-subword-construction-geez/) 24. **[FLEXITOKENS: Flexible Tokenization for Evolving Language Models](https://md.research.elamur.ai/free/en/papers/owodunni-2026-flexitokens-flexible-tokenization-evolving-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Abraham Toluwase Owodunni, Orevaoghene Ahia, Sachin Kumar*2026 · 3 citations* 61 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/al-rfou-2018-character-level-language-modeling-deeper/) — Character-Level Language Modeling with Deeper Self-Attention