  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.
