Detecting Bias in Transfer Learning Approaches for Text Classification
This work addresses the problem of bias in transfer learning for text classification, which is important for researchers and practitioners working with imbalanced datasets in NLP.
This paper evaluates existing transfer learning methods for detecting bias in imbalanced classes within text classification. The authors also propose a new method to address the domain class imbalance problem.
Classification is an essential and fundamental task in machine learning, playing a cardinal role in the field of natural language processing (NLP) and computer vision (CV). In a supervised learning setting, labels are always needed for the classification task. Especially for deep neural models, a large amount of high-quality labeled data are required for training. However, when a new domain comes out, it is usually hard or expensive to acquire the labels. Transfer learning could be an option to transfer the knowledge from a source domain to a target domain. A challenge is that these two domains can be different, either on the feature distribution, or the class distribution for the nature of the samples. In this work, we evaluate some existing transfer learning approaches on detecting the bias of imbalanced classes including traditional and deep models. Besides, we propose an approach to bridge the gap of the domain class imbalance issue.