Text classification is a widely studied problem and has broad applications. In many real-world problems, the number of texts for training classification models is limited, which renders these models prone to overfitting. To address this problem, we propose SSL-Reg, a data-dependent regularization approach based on self-supervised learning (SSL). SSL is an unsupervised learning approach which defines auxiliary tasks on input data without using any human-provided labels and learns data representations by solving these auxiliary tasks. In SSL-Reg, a supervised classification task and an unsupervised SSL task are performed simultaneously. The SSL task is unsupervised, which is defined purely on input texts without using any human-provided labels. Training a model using an SSL task can prevent the model from being overfitted to a limited number of class labels in the classification task. Experiments on 17 text classification datasets demonstrate the effectiveness of our proposed method. Code is available at https://github.com/ UCSD-AI4H/SSReg

Self-supervised Regularization for Text Classification



<i>**Session Chair: ** Arman Cohan </i><br><br>

15B-Oral: Machine Learning for NLP: Classification and Structured Prediction Models

technical paper

2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

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Self-supervised Regularization for Text Classification

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