Uncertainty-aware Parameter-Efficient Self-training for Semi-supervised
  Language Understanding
Jianing Wang Qiushi Sun Nuo Chen Chengyu Wang Jun Huang Ming Gao Xiang Li
The recent success of large pre-trained language models (PLMs) heavily hinges
on massive labeled data, which typically produces inferior performance in
low-resource scenarios. To remedy this dilemma, we study self-training as one
of the predominant semi-supervised learning (SSL) approaches, which utilizes
large-scale unlabeled data to generate synthetic examples. However, too many
noisy labels will hurt the model performance, and the self-training procedure
requires multiple training iterations making it more expensive if all the model
parameters of the PLM are updated. This paper presents UPET, a novel
Uncertainty-aware Parameter-Efficient self-Training framework to effectively
and efficiently address the labeled data scarcity issue. Specifically, we
incorporate Monte Carlo (MC) dropout in Bayesian neural network (BNN) to
perform uncertainty estimation for the teacher model and then judiciously
select reliable pseudo-labeled examples based on confidence and certainty.
During the student training, we introduce multiple parameter-efficient learning
(PEL) paradigms that allow the optimization of only a small percentage of
parameters. We also propose a novel Easy-Hard Contrastive Tuning to enhance the
robustness and generalization. Extensive experiments over multiple downstream
tasks demonstrate that UPET achieves a substantial improvement in terms of
performance and efficiency. Our codes and data are released at https:
//github.com/wjn1996/UPET.