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

2022-10-23 08:19:58
Hui Chen, Wei Han, Soujanya Poria

Abstract

Self-training methods have been explored in recent years and have exhibited great performance in improving semi-supervised learning. This work presents a Simple instance-Adaptive self-Training method (SAT) for semi-supervised text classification. SAT first generates two augmented views for each unlabeled data and then trains a meta-learner to automatically identify the relative strength of augmentations based on the similarity between the original view and the augmented views. The weakly-augmented view is fed to the model to produce a pseudo-label and the strongly-augmented view is used to train the model to predict the same pseudo-label. We conducted extensive experiments and analyses on three text classification datasets and found that with varying sizes of labeled training data, SAT consistently shows competitive performance compared to existing semi-supervised learning methods. Our code can be found at \url{this https URL}.

Abstract (translated)

URL

https://arxiv.org/abs/2210.12653

PDF

https://arxiv.org/pdf/2210.12653.pdf


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