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Self-training methods have been explored in recent years and have exhibitedgreat performance in improving semi-supervised learning. This work presents aSimple instance-Adaptive self-Training method (SAT) for semi-supervised textclassification. SAT first generates two augmented views for each unlabeled dataand then trains a meta-learner to automatically identify the relative strengthof augmentations based on the similarity between the original view and theaugmented views. The weakly-augmented view is fed to the model to produce apseudo-label and the strongly-augmented view is used to train the model topredict the same pseudo-label. We conducted extensive experiments and analyseson three text classification datasets and found that with varying sizes oflabeled training data, SAT consistently shows competitive performance comparedto existing semi-supervised learning methods. Our code can be found at\url{https://github.com/declare-lab/SAT.git}.

SAT: Improving Semi-Supervised Text Classification with Simple Instance-Adaptive Self-Training