This project contains the implementation of PCM for semi-supervised text classification, as presented in our paper
Progressive Class Semantic Matching for Semi-supervised Text Classification,
Hai-Ming Xu, Lingqiao Liu and Ehsan Abbasnejad,
To be appeared in NAACL 2022
The full paper is available at: Open Review Link
- [2022-04-23] Repo is created. Code will come soon.
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