---
title: "Who cites Subword Regularization: Improving Neural Network…"
description: "561 papers of the Elamur Research library cite «Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates»…"
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# Who cites «Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates»

561 papers of our library cite it (of the 140,884 whose bibliographies we read) · 56 with a page here, the most cited first · [← the paper](https://md.research.elamur.ai/free/en/papers/kudo-2018-subword-regularization-improving-neural-network/)

1. [**Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer**Colin Raffel, Noam Shazeer, Adam Roberts, et al.*2019 · 7,433 citations*](https://md.research.elamur.ai/free/en/papers/raffel-2019-exploring-limits-transfer-learning-unified/)
2. [**A Survey of Large Language Models**Wayne Xin Zhao, Kun Zhou, Junyi Li, et al.*2023 · 2,847 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/)
3. [**PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization**Jingqing Zhang, Yao Zhao, Mohammad Saleh, et al.*2019 · 1,806 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2019-pegasus-pre-training-extracted-gap/)
4. [**mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer**Linting Xue, Noah Constant, Adam Roberts, et al.*2021 · 1,574 citations*](https://md.research.elamur.ai/free/en/papers/xue-2021-mt5-massively-multilingual-pre-trained/)
5. [**DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing**Pengcheng He, Jianfeng Gao, Weizhu Chen*2021 · 1,316 citations*](https://md.research.elamur.ai/free/en/papers/he-2021-debertav3-improving-deberta-using-electra/)
6. [**AraBERT: Transformer-based Model for Arabic Language Understanding**Wissam Antoun, Fady Baly, Hazem Hajj*2020 · 1,297 citations*](https://md.research.elamur.ai/free/en/papers/antoun-2020-arabert-transformer-based-model-arabic/)
7. [**CamemBERT: a Tasty French Language Model**Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suárez, et al.*2019 · 1,262 citations*](https://md.research.elamur.ai/free/en/papers/martin-2019-camembert-tasty-french-language-model/)
8. [**CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data**Guillaume Wenzek, Marie-Anne Lachaux, Alexis Conneau, et al.*2020 · 650 citations*](https://md.research.elamur.ai/free/en/papers/wenzek-2020-ccnet-extracting-high-quality-monolingual/)
9. [**CoCa: Contrastive Captioners are Image-Text Foundation Models**Jiahui Yu, Zirui Wang, Vijay Vasudevan, et al.*2022 · 646 citations*](https://md.research.elamur.ai/free/en/papers/yu-2022-coca-contrastive-captioners-image-text/)
10. [**BloombergGPT: A Large Language Model for Finance**Shijie Wu, Ozan Irsoy, Steven Lu, et al.*2023 · 608 citations*](https://md.research.elamur.ai/free/en/papers/wu-2023-bloomberggpt-large-language-model-finance/)
11. [**Few-shot Learning with Multilingual Language Models**Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, et al.*2021 · 496 citations*](https://md.research.elamur.ai/free/en/papers/lin-2021-few-shot-learning-multilingual-language/)
12. [**Are All Languages Created Equal in Multilingual BERT?**Shijie Wu, Mark Dredze*2020 · 458 citations*](https://md.research.elamur.ai/free/en/papers/wu-2020-are-all-languages-created-equal/)
13. [**BPE-Dropout: Simple and Effective Subword Regularization**Ivan Provilkov, Dmitrii Emelianenko, Elena Voita*2020 · 389 citations*](https://md.research.elamur.ai/free/en/papers/provilkov-2020-bpe-dropout-simple-effective-subword/)
14. [**Choosing Transfer Languages for Cross-Lingual Learning**Yu-Hsiang Lin, Chian-Yu Chen, Jean Lee, et al.*2019 · 313 citations*](https://md.research.elamur.ai/free/en/papers/lin-2019-choosing-transfer-languages-cross-lingual/)
15. [**Challenges and Applications of Large Language Models**Jean Kaddour, Joshua Harris, Maximilian Mozes, et al.*2023 · 300 citations*](https://md.research.elamur.ai/free/en/papers/kaddour-2023-challenges-applications-large-language-models/)
16. [**Survey of Low-Resource Machine Translation**Barry Haddow, Rachel Bawden, Antonio Valerio Miceli Barone, et al.*2022 · 251 citations*](https://md.research.elamur.ai/free/en/papers/haddow-2022-survey-low-resource-machine-translation/)
17. [**Pretrained Language Models for Biomedical and Clinical Tasks: Understanding and Extending the State-of-the-Art**Patrick Lewis, Myle Ott, Jingfei Du, et al.*2020 · 240 citations*](https://md.research.elamur.ai/free/en/papers/lewis-2020-pretrained-language-models-biomedical-clinical/)
18. [**AlBERTo: Italian BERT Language Understanding Model for NLP Challenging Tasks Based on Tweets**Marco Polignano, Pierpaolo Basile, Marco de Gemmis, et al.*2019 · 228 citations*](https://md.research.elamur.ai/free/en/papers/polignano-2019-alberto-italian-bert-language-understanding/)
19. [**Baseline Defenses for Adversarial Attacks Against Aligned Language Models**Neel Jain, Avi Schwarzschild, Yuxin Wen, et al.*2023 · 224 citations*](https://md.research.elamur.ai/free/en/papers/jain-2023-baseline-defenses-adversarial-attacks-against/)
20. [**Reinforced Self-Training (ReST) for Language Modeling**Caglar Gulcehre, Tom Le Paine, Srivatsan Srinivasan, et al.*2023 · 209 citations*](https://md.research.elamur.ai/free/en/papers/gulcehre-2023-reinforced-self-training-rest-language/)
21. [**On Exposure Bias, Hallucination and Domain Shift in Neural Machine Translation**Chaojun Wang, Rico Sennrich*2020 · 204 citations*](https://md.research.elamur.ai/free/en/papers/wang-2020-exposure-bias-hallucination-domain-shift/)
22. [**AraT5: Text-to-Text Transformers for Arabic Language Generation**El Moatez Billah Nagoudi, AbdelRahim Elmadany, Muhammad Abdul-Mageed*2022 · 201 citations*](https://md.research.elamur.ai/free/en/papers/nagoudi-2022-arat5-text-text-transformers-arabic/)
23. [**CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languages**Thuat Nguyen, Chien Van Nguyen, Viet Dac Lai, et al.*2023 · 179 citations*](https://md.research.elamur.ai/free/en/papers/nguyen-2023-culturax-cleaned-enormous-multilingual-dataset/)
24. [**Charformer: Fast Character Transformers via Gradient-based Subword Tokenization**Yi Tay, Vinh Q. Tran, Sebastian Ruder, et al.*2021 · 179 citations*](https://md.research.elamur.ai/free/en/papers/tay-2021-charformer-fast-character-transformers-gradient/)
25. [**Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language Processing**Minh Van Nguyen, Viet Dac Lai, Amir Pouran Ben Veyseh, et al.*2021 · 169 citations*](https://md.research.elamur.ai/free/en/papers/nguyen-2021-trankit-light-weight-transformer-based/)
26. [**Do All Languages Cost the Same? Tokenization in the Era of Commercial Language Models**Orevaoghene Ahia, Sachin Kumar, Hila Gonen, et al.*2023 · 164 citations*](https://md.research.elamur.ai/free/en/papers/ahia-2023-do-all-languages-cost-same/)
27. [**Larger-Scale Transformers for Multilingual Masked Language Modeling**Naman Goyal, Jingfei Du, Myle Ott, et al.*2021 · 160 citations*](https://md.research.elamur.ai/free/en/papers/goyal-2021-larger-scale-transformers-multilingual-masked/)
28. [**Testing the Predictions of Surprisal Theory in 11 Languages**Ethan G. Wilcox, Tiago Pimentel, Clara Meister, et al.*2023 · 158 citations*](https://md.research.elamur.ai/free/en/papers/wilcox-2023-testing-predictions-surprisal-theory-11/)
29. [**Canine: Pre-training an Efficient Tokenization-Free Encoder for Language Representation**Jonathan H. Clark, Dan Garrette, Iulia Turc, et al.*2022 · 156 citations*](https://md.research.elamur.ai/free/en/papers/clark-2022-canine-pre-training-efficient-tokenization/)
30. [**A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translation**David Ifeoluwa Adelani, Jesujoba Oluwadara Alabi, Angela Fan, et al.*2022 · 155 citations*](https://md.research.elamur.ai/free/en/papers/adelani-2022-few-thousand-translations-go-long/)
31. [**Language Model Tokenizers Introduce Unfairness Between Languages**Aleksandar Petrov, Emanuele La Malfa, Philip H. S. Torr, et al.*2023 · 149 citations*](https://md.research.elamur.ai/free/en/papers/petrov-2023-language-model-tokenizers-introduce-unfairness/)
32. [**The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset**Hugo Laurençon, Lucile Saulnier, Thomas Wang, et al.*2023 · 145 citations*](https://md.research.elamur.ai/free/en/papers/lauren-ccon-2023-bigscience-roots-corpus-1-6tb/)
33. [**Byte Pair Encoding is Suboptimal for Language Model Pretraining**Kaj Bostrom, Greg Durrett*2020 · 136 citations*](https://md.research.elamur.ai/free/en/papers/bostrom-2020-byte-pair-encoding-is-suboptimal/)
34. [**Domain Robustness in Neural Machine Translation**Mathias Müller, Annette Rios, Rico Sennrich*2020 · 135 citations*](https://md.research.elamur.ai/free/en/papers/muller-2020-domain-robustness-neural-machine-translation/)
35. [**Tokenizer Choice For LLM Training: Negligible or Crucial?**Mehdi Ali, Michael Fromm, Klaudia Thellmann, et al.*2024 · 102 citations*](https://md.research.elamur.ai/free/en/papers/ali-2024-tokenizer-choice-llm-training-negligible/)
36. [**Language Modeling Is Compression**Gregoire Deletang, Anian Ruoss, Paul-Ambroise Duquenne, et al.*2023 · 89 citations*](https://md.research.elamur.ai/free/en/papers/deletang-2023-language-modeling-compression/)
37. [**Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP**Sabrina J. Mielke, Zaid Alyafeai, Elizabeth Salesky, et al.*2021 · 89 citations*](https://md.research.elamur.ai/free/en/papers/mielke-2021-between-words-characters-brief-history/)
38. [**Arabic Speech Recognition by End-to-End, Modular Systems and Human**Amir Hussein, Shinji Watanabe, Ahmed Ali*2021 · 76 citations*](https://md.research.elamur.ai/free/en/papers/hussein-2021-arabic-speech-recognition-end-end/)
39. [**Finding the Optimal Vocabulary Size for Neural Machine Translation**Thamme Gowda, Jonathan May*2020 · 72 citations*](https://md.research.elamur.ai/free/en/papers/gowda-2020-finding-optimal-vocabulary-size-neural/)
40. [**The SIGMORPHON 2022 Shared Task on Morpheme Segmentation**Khuyagbaatar Batsuren, Gabor Bella, Aryaman Arora*2022 · 71 citations*](https://md.research.elamur.ai/free/en/papers/batsuren-2022-morpheme-segmentation/)
41. [**Tokenization Is More Than Compression**Craig W Schmidt, Varshini Reddy, Haoran Zhang, et al.*2024 · 66 citations*](https://md.research.elamur.ai/free/en/papers/schmidt-2024-tokenization-is-more-than-compression/)
42. [**Tokenization counts: the impact of tokenization on arithmetic in frontier LLMs**Aaditya K. Singh, DJ Strouse*2024 · 53 citations*](https://md.research.elamur.ai/free/en/papers/singh-2024-tokenization-counts-impact-tokenization-arithmetic/)
43. [**Getting the most out of your tokenizer for pre-training and domain adaptation**Gautier Dagan, Gabriel Synnaeve, Baptiste Roziere*2024 · 44 citations*](https://md.research.elamur.ai/free/en/papers/dagan-2024-getting-most-out-tokenizer-pre/)
44. [**Beyond Correlation: Interpretable Evaluation of Machine Translation Metrics**Stefano Perrella, Lorenzo Proietti, Pere-Lluís Huguet Cabot, et al.*2024 · 15 citations*](https://md.research.elamur.ai/free/en/papers/perrella-2024-beyond-correlation-interpretable-evaluation-machine/)
45. [**The Impact of Token Granularity on the Predictive Power of Language Model Surprisal**Byung-Doh Oh, William Schuler*2025 · 12 citations*](https://md.research.elamur.ai/free/en/papers/oh-2025-impact-token-granularity-predictive-power/)
46. [**Optimizing the Size of Subword Vocabularies in Dialect Classification**Vani Kanjirangat, Tanja Samardžić, Ljiljana Dolamic, et al.*2023 · 11 citations*](https://md.research.elamur.ai/free/en/papers/kanjirangat-2023-optimizing-size-subword-vocabularies-dialect/)
47. [**Exploring Tokenization Strategies and Vocabulary Sizes for Enhanced Arabic Language Models**Mohamed Taher Alrefaie, Nour Eldin Morsy, Nada Samir*2024 · 5 citations*](https://md.research.elamur.ai/free/en/papers/alrefaie-2024-exploring-tokenization-strategies-vocabulary-sizes/)
48. [**Morphemes Without Borders: Evaluating Root-Pattern Morphology in Arabic Tokenizers and LLMs**Yara Alakeel, Chatrine Qwaider, Hanan Aldarmaki, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/alakeel-2026-morphemes-borders-evaluating-root-pattern/)
49. [**Morphemes without Borders: Evaluating Root–Pattern Morphology in Arabic Tokenizers and LLMs**Yara Yousif Alakeel, Chatrine Qwaider, Hanan Aldarmaki, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/alakeel-2026-morphemes-without-borders-evaluating-root/)
50. [**Tokenisation in Machine Translation Does Matter: The impact of different tokenisation approaches for Maltese**Kurt Abela, Kurt Micallef, Marc Tanti, et al.*2024 · 1 citation*](https://md.research.elamur.ai/free/en/papers/abela-2024-tokenisation-machine-translation-does-matter/)
51. [**Bringing Mapudungun into the Modern MT Ecosystem: Morphology-Aware Tokenization for NLLB-200 Fine-Tuning**Isaac Thompson, Brandon Rogers, Eric Ringger*2026*](https://md.research.elamur.ai/free/en/papers/thompson-2026-bringing-mapudungun-into-modern-mt/)
52. [**SemToken: Semantic-Aware Tokenization for Efficient Long-Context Language Models**Dong Liu, Yanxuan Yu*2026*](https://md.research.elamur.ai/free/en/papers/liu-2026-semtoken-semantic-aware-tokenization-efficient/)
53. [**Supplementary Resources and Analysis for Automatic Speech Recognition Systems Trained on the Loquacious Dataset**Nick Rossenbach, Robin Schmitt, Tina Raissi, et al.*2026*](https://md.research.elamur.ai/free/en/papers/rossenbach-2026-supplementary-resources-analysis-automatic-speech/)
54. [**The Impact of Tokenization Algorithms on Hungarian Language Model Performance**Mátyás Osváth, Máté Norbert Molnár, Roland Gunics, et al.*2026*](https://md.research.elamur.ai/free/en/papers/osvath-2026-impact-tokenization-algorithms-hungarian-language/)
55. [**AraToken: Optimizing Arabic Tokenization with Normalization Pipeline and Language Extension for Qwen3**Mark Kashirskiy, Artiom Lipinski, Ilya Makarov*2025*](https://md.research.elamur.ai/free/en/papers/kashirskiy-2025-aratoken-optimizing-arabic-tokenization-normalization/)
56. [**Semitic Root Encoding: Tokenization Based on the Templatic Morphology of Semitic Languages in NMT**Brendan T. Hatch, Stephen D. Richardson*2025*](https://md.research.elamur.ai/free/en/papers/hatch-2025-semitic-root-encoding-tokenization-based/)

505 more papers of our library cite it; they have no page here yet.

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