Progressive Class Semantic Matching for Semi-supervised Text Classification
This work addresses the need for efficient text classification with less labeled data, offering an incremental improvement over existing semi-supervised approaches.
The paper tackles the problem of reducing annotation costs in text classification by proposing a semi-supervised learning method that leverages pre-trained language models for topic matching, resulting in state-of-the-art performance with improved stability and remarkable gains over baselines.
Semi-supervised learning is a promising way to reduce the annotation cost for text-classification. Combining with pre-trained language models (PLMs), e.g., BERT, recent semi-supervised learning methods achieved impressive performance. In this work, we further investigate the marriage between semi-supervised learning and a pre-trained language model. Unlike existing approaches that utilize PLMs only for model parameter initialization, we explore the inherent topic matching capability inside PLMs for building a more powerful semi-supervised learning approach. Specifically, we propose a joint semi-supervised learning process that can progressively build a standard $K$-way classifier and a matching network for the input text and the Class Semantic Representation (CSR). The CSR will be initialized from the given labeled sentences and progressively updated through the training process. By means of extensive experiments, we show that our method can not only bring remarkable improvement to baselines, but also overall be more stable, and achieves state-of-the-art performance in semi-supervised text classification.