Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach

15 Oct 2020 Yue Yu Simiao Zuo Haoming Jiang Wendi Ren Tuo Zhao Chao Zhang

Fine-tuned pre-trained language models (LMs) achieve enormous success in many natural language processing (NLP) tasks, but they still require excessive labeled data in the fine-tuning stage. We study the problem of fine-tuning pre-trained LMs using only weak supervision, without any labeled data... (read more)

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