Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Self-supervised Regularization for Text Classification   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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The categories of this paper are: mtrl-th
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The categories of this paper are: stat
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The categories of this paper are: nucl-th
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The categories of this paper are: comp-gas
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The categories of this paper are: gr-qc
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The categories of this paper are: funct-an
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The categories of this paper are: dg-ga
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The categories of this paper are: nucl-ex
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The categories of this paper are: cond-mat
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The categories of this paper are: adap-org
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The categories of this paper are: alg-geom
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The categories of this paper are: solv-int
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The categories of this paper are: eess
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The categories of this paper are: econ
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The categories of this paper are: hep-ph
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The categories of this paper are: quant-ph