Self-supervised Regularization for Text Classification

• URL: http://arxiv.org/abs/2103.05231v1
• Date: Tue, 9 Mar 2021 05:35:52 GMT
• Title: Self-supervised Regularization for Text Classification
• Authors: Meng Zhou, Zechen Li, Pengtao Xie
• Abstract summary: In many real-world problems, the number of texts for training classification models is limited, which renders these models prone to overfitting. 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.
• Score: 14.824073299035675
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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