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        <title>SAT: Improving Semi-Supervised Text Classification with Simple Instance-Adaptive Self-Training</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T SAT: Improving Semi-Supervised Text Classification with Simple Instance-Adaptive Self-Training
%A Chen, Hui
%A Han, Wei
%A Poria, Soujanya
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F chen-etal-2022-sat
%X 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.
%R 10.18653/v1/2022.findings-emnlp.456
%U https://aclanthology.org/2022.findings-emnlp.456/
%U https://doi.org/10.18653/v1/2022.findings-emnlp.456
%P 6141-6146

Markdown (Informal)

[SAT: Improving Semi-Supervised Text Classification with Simple Instance-Adaptive Self-Training](https://aclanthology.org/2022.findings-emnlp.456/) (Chen et al., Findings 2022)

• SAT: Improving Semi-Supervised Text Classification with Simple Instance-Adaptive Self-Training (Chen et al., Findings 2022)

ACL

• Hui Chen, Wei Han, and Soujanya Poria. 2022. SAT: Improving Semi-Supervised Text Classification with Simple Instance-Adaptive Self-Training. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 6141–6146, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.