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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 (Devlin et al., 2019a) is an unsupervised learning approach that 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.
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Using Machine Learning to Aid in Data Classification: Classifying Occupation Compatibility with Highly Automated Vehicles
Data classification is central to human factors research, and manual data classification is tedious and error prone. Supervised learning enables analysts to train an algorithm by manually classifying a few cases and then have that algorithm classify many cases. However, algorithms often fail to leverage human insight. To address this, we augment supervised learning with unsupervised learning and data visualization. Unsupervised learning highlights potential classification errors, explains the underlying classification, and identifies additional cases that merit manual classification. We illustrate this using the Occupational Information Network database to classify occupations as having tasks that might be performed in an automated vehicle.
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- Award ID(s):
- 1839484
- PAR ID:
- 10546753
- Publisher / Repository:
- SAGE Publications
- Date Published:
- Journal Name:
- Ergonomics in Design: The Quarterly of Human Factors Applications
- Volume:
- 29
- Issue:
- 2
- ISSN:
- 1064-8046
- Format(s):
- Medium: X Size: p. 4-12
- Size(s):
- p. 4-12
- Sponsoring Org:
- National Science Foundation
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