• Corpus ID: 248506228

AcTune: Uncertainty-aware Active Self-Training for Semi-Supervised Active Learning with Pretrained Language Models

  title={AcTune: Uncertainty-aware Active Self-Training for Semi-Supervised Active Learning with Pretrained Language Models},
  author={Yue Yu and Lingkai Kong and Jieyu Zhang and Rongzhi Zhang and Chao Zhang},
While pre-trained language model (PLM) finetuning has achieved strong performance in many NLP tasks, the fine-tuning stage can be still demanding in labeled data. Recent works have resorted to active fine-tuning to improve the label efficiency of PLM fine-tuning, but none of them investigate the potential of unlabeled data. We propose A C T UNE , a new framework that leverages unlabeled data to improve the label efficiency of active PLM fine-tuning. A C T UNE switches between data annotation and model… 
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