Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach
This addresses the need for labeled data in fine-tuning language models for NLP tasks, offering a solution that reduces annotation costs, though it is incremental as it builds on existing weak supervision and self-training techniques.
The paper tackles the problem of fine-tuning pre-trained language models using only weak supervision, without labeled data, by developing a contrastive self-training framework called COSINE. The result is that COSINE outperforms the strongest baseline by large margins on 7 benchmarks in 6 tasks and achieves competitive performance with fully-supervised methods.
Fine-tuned pre-trained language models (LMs) have achieved 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. This problem is challenging because the high capacity of LMs makes them prone to overfitting the noisy labels generated by weak supervision. To address this problem, we develop a contrastive self-training framework, COSINE, to enable fine-tuning LMs with weak supervision. Underpinned by contrastive regularization and confidence-based reweighting, this contrastive self-training framework can gradually improve model fitting while effectively suppressing error propagation. Experiments on sequence, token, and sentence pair classification tasks show that our model outperforms the strongest baseline by large margins on 7 benchmarks in 6 tasks, and achieves competitive performance with fully-supervised fine-tuning methods.