Who cites «Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates»
- Exploring the Limits of Transfer Learning with a Unified Text-to-Text TransformerColin Raffel, Noam Shazeer, Adam Roberts, et al.2019 · 7,433 citations
- A Survey of Large Language ModelsWayne Xin Zhao, Kun Zhou, Junyi Li, et al.2023 · 2,847 citations
- PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive SummarizationJingqing Zhang, Yao Zhao, Mohammad Saleh, et al.2019 · 1,806 citations
- mT5: A Massively Multilingual Pre-trained Text-to-Text TransformerLinting Xue, Noah Constant, Adam Roberts, et al.2021 · 1,574 citations
- DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding SharingPengcheng He, Jianfeng Gao, Weizhu Chen2021 · 1,316 citations
- AraBERT: Transformer-based Model for Arabic Language UnderstandingWissam Antoun, Fady Baly, Hazem Hajj2020 · 1,297 citations
- CamemBERT: a Tasty French Language ModelLouis Martin, Benjamin Muller, Pedro Javier Ortiz Suárez, et al.2019 · 1,262 citations
- CCNet: Extracting High Quality Monolingual Datasets from Web Crawl DataGuillaume Wenzek, Marie-Anne Lachaux, Alexis Conneau, et al.2020 · 650 citations
- CoCa: Contrastive Captioners are Image-Text Foundation ModelsJiahui Yu, Zirui Wang, Vijay Vasudevan, et al.2022 · 646 citations
- BloombergGPT: A Large Language Model for FinanceShijie Wu, Ozan Irsoy, Steven Lu, et al.2023 · 608 citations
- Few-shot Learning with Multilingual Language ModelsXi Victoria Lin, Todor Mihaylov, Mikel Artetxe, et al.2021 · 496 citations
- Are All Languages Created Equal in Multilingual BERT?Shijie Wu, Mark Dredze2020 · 458 citations
- BPE-Dropout: Simple and Effective Subword RegularizationIvan Provilkov, Dmitrii Emelianenko, Elena Voita2020 · 389 citations
- Choosing Transfer Languages for Cross-Lingual LearningYu-Hsiang Lin, Chian-Yu Chen, Jean Lee, et al.2019 · 313 citations
- Challenges and Applications of Large Language ModelsJean Kaddour, Joshua Harris, Maximilian Mozes, et al.2023 · 300 citations
- Survey of Low-Resource Machine TranslationBarry Haddow, Rachel Bawden, Antonio Valerio Miceli Barone, et al.2022 · 251 citations
- Pretrained Language Models for Biomedical and Clinical Tasks: Understanding and Extending the State-of-the-ArtPatrick Lewis, Myle Ott, Jingfei Du, et al.2020 · 240 citations
- AlBERTo: Italian BERT Language Understanding Model for NLP Challenging Tasks Based on TweetsMarco Polignano, Pierpaolo Basile, Marco de Gemmis, et al.2019 · 228 citations
- Baseline Defenses for Adversarial Attacks Against Aligned Language ModelsNeel Jain, Avi Schwarzschild, Yuxin Wen, et al.2023 · 224 citations
- Reinforced Self-Training (ReST) for Language ModelingCaglar Gulcehre, Tom Le Paine, Srivatsan Srinivasan, et al.2023 · 209 citations
- On Exposure Bias, Hallucination and Domain Shift in Neural Machine TranslationChaojun Wang, Rico Sennrich2020 · 204 citations
- AraT5: Text-to-Text Transformers for Arabic Language GenerationEl Moatez Billah Nagoudi, AbdelRahim Elmadany, Muhammad Abdul-Mageed2022 · 201 citations
- CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 LanguagesThuat Nguyen, Chien Van Nguyen, Viet Dac Lai, et al.2023 · 179 citations
- Charformer: Fast Character Transformers via Gradient-based Subword TokenizationYi Tay, Vinh Q. Tran, Sebastian Ruder, et al.2021 · 179 citations
- Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language ProcessingMinh Van Nguyen, Viet Dac Lai, Amir Pouran Ben Veyseh, et al.2021 · 169 citations
- Do All Languages Cost the Same? Tokenization in the Era of Commercial Language ModelsOrevaoghene Ahia, Sachin Kumar, Hila Gonen, et al.2023 · 164 citations
- Larger-Scale Transformers for Multilingual Masked Language ModelingNaman Goyal, Jingfei Du, Myle Ott, et al.2021 · 160 citations
- Testing the Predictions of Surprisal Theory in 11 LanguagesEthan G. Wilcox, Tiago Pimentel, Clara Meister, et al.2023 · 158 citations
- Canine: Pre-training an Efficient Tokenization-Free Encoder for Language RepresentationJonathan H. Clark, Dan Garrette, Iulia Turc, et al.2022 · 156 citations
- A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News TranslationDavid Ifeoluwa Adelani, Jesujoba Oluwadara Alabi, Angela Fan, et al.2022 · 155 citations
- Language Model Tokenizers Introduce Unfairness Between LanguagesAleksandar Petrov, Emanuele La Malfa, Philip H. S. Torr, et al.2023 · 149 citations
- The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual DatasetHugo Laurençon, Lucile Saulnier, Thomas Wang, et al.2023 · 145 citations
- Byte Pair Encoding is Suboptimal for Language Model PretrainingKaj Bostrom, Greg Durrett2020 · 136 citations
- Domain Robustness in Neural Machine TranslationMathias Müller, Annette Rios, Rico Sennrich2020 · 135 citations
- Tokenizer Choice For LLM Training: Negligible or Crucial?Mehdi Ali, Michael Fromm, Klaudia Thellmann, et al.2024 · 102 citations
- Language Modeling Is CompressionGregoire Deletang, Anian Ruoss, Paul-Ambroise Duquenne, et al.2023 · 89 citations
- Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLPSabrina J. Mielke, Zaid Alyafeai, Elizabeth Salesky, et al.2021 · 89 citations
- Arabic Speech Recognition by End-to-End, Modular Systems and HumanAmir Hussein, Shinji Watanabe, Ahmed Ali2021 · 76 citations
- Finding the Optimal Vocabulary Size for Neural Machine TranslationThamme Gowda, Jonathan May2020 · 72 citations
- The SIGMORPHON 2022 Shared Task on Morpheme SegmentationKhuyagbaatar Batsuren, Gabor Bella, Aryaman Arora2022 · 71 citations
- Tokenization Is More Than CompressionCraig W Schmidt, Varshini Reddy, Haoran Zhang, et al.2024 · 66 citations
- Tokenization counts: the impact of tokenization on arithmetic in frontier LLMsAaditya K. Singh, DJ Strouse2024 · 53 citations
- Getting the most out of your tokenizer for pre-training and domain adaptationGautier Dagan, Gabriel Synnaeve, Baptiste Roziere2024 · 44 citations
- Beyond Correlation: Interpretable Evaluation of Machine Translation MetricsStefano Perrella, Lorenzo Proietti, Pere-Lluís Huguet Cabot, et al.2024 · 15 citations
- The Impact of Token Granularity on the Predictive Power of Language Model SurprisalByung-Doh Oh, William Schuler2025 · 12 citations
- Optimizing the Size of Subword Vocabularies in Dialect ClassificationVani Kanjirangat, Tanja Samardžić, Ljiljana Dolamic, et al.2023 · 11 citations
- Exploring Tokenization Strategies and Vocabulary Sizes for Enhanced Arabic Language ModelsMohamed Taher Alrefaie, Nour Eldin Morsy, Nada Samir2024 · 5 citations
- Morphemes Without Borders: Evaluating Root-Pattern Morphology in Arabic Tokenizers and LLMsYara Alakeel, Chatrine Qwaider, Hanan Aldarmaki, et al.2026 · 1 citation
- Morphemes without Borders: Evaluating Root–Pattern Morphology in Arabic Tokenizers and LLMsYara Yousif Alakeel, Chatrine Qwaider, Hanan Aldarmaki, et al.2026 · 1 citation
- Tokenisation in Machine Translation Does Matter: The impact of different tokenisation approaches for MalteseKurt Abela, Kurt Micallef, Marc Tanti, et al.2024 · 1 citation
- Bringing Mapudungun into the Modern MT Ecosystem: Morphology-Aware Tokenization for NLLB-200 Fine-TuningIsaac Thompson, Brandon Rogers, Eric Ringger2026
- SemToken: Semantic-Aware Tokenization for Efficient Long-Context Language ModelsDong Liu, Yanxuan Yu2026
- Supplementary Resources and Analysis for Automatic Speech Recognition Systems Trained on the Loquacious DatasetNick Rossenbach, Robin Schmitt, Tina Raissi, et al.2026
- The Impact of Tokenization Algorithms on Hungarian Language Model PerformanceMátyás Osváth, Máté Norbert Molnár, Roland Gunics, et al.2026
- AraToken: Optimizing Arabic Tokenization with Normalization Pipeline and Language Extension for Qwen3Mark Kashirskiy, Artiom Lipinski, Ilya Makarov2025
- Semitic Root Encoding: Tokenization Based on the Templatic Morphology of Semitic Languages in NMTBrendan T. Hatch, Stephen D. Richardson2025
This page in Markdown → https://md.research.elamur.ai/free/en/papers/kudo-2018-subword-regularization-improving-neural-network/cited-by/