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Who cites «To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks»

109 papers of our library cite it (of the 149,439 whose bibliographies we read) · 19 with a page here, the most cited first · ← back to «To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks»

  1. Exploring the Limits of Transfer Learning with a Unified Text-to-Text TransformerarXiv:1910.10683Colin Raffel, Noam Shazeer, Adam Roberts, et al.2019 · 11,325 citations
  2. Pre-trained Models for Natural Language Processing: A SurveyarXiv:2003.08271 · SCIENCE CHINA Technological Sciences,2020, 63, 1872-1897Xipeng Qiu, Tianxiang Sun, Yige Xu, et al.2020 · 1,759 citations
  3. Revisiting Few-sample BERT Fine-tuningarXiv:2006.05987Tianyi Zhang, Felix Wu, Arzoo Katiyar, et al.2020 · 368 citations
  4. 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
  5. Analyzing Dataset Annotation Quality Management in the WildComputational LinguisticsJan-Christoph Klie, Richard Eckart de Castilho, Iryna Gurevych2024 · 76 citations
  6. Annotation Error Detection: Analyzing the Past and Present for a More Coherent FutureJan-Christoph Klie, Bonnie Webber, Iryna Gurevych2023 · 73 citations
  7. Large-scale text pre-training helps with dialogue act recognition, but not without fine-tuningBill Noble, Vladislav Maraev2021 · 7 citations

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

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