JointMatch: A Unified Approach for Diverse and Collaborative
  Pseudo-Labeling to Semi-Supervised Text Classification
Henry Peng Zou Cornelia Caragea
Semi-supervised text classification (SSTC) has gained increasing attention
due to its ability to leverage unlabeled data. However, existing approaches
based on pseudo-labeling suffer from the issues of pseudo-label bias and error
accumulation. In this paper, we propose JointMatch, a holistic approach for
SSTC that addresses these challenges by unifying ideas from recent
semi-supervised learning and the task of learning with noise. JointMatch
adaptively adjusts classwise thresholds based on the learning status of
different classes to mitigate model bias towards current easy classes.
Additionally, JointMatch alleviates error accumulation by utilizing two
differently initialized networks to teach each other in a cross-labeling
manner. To maintain divergence between the two networks for mutual learning, we
introduce a strategy that weighs more disagreement data while also allowing the
utilization of high-quality agreement data for training. Experimental results
on benchmark datasets demonstrate the superior performance of JointMatch,
achieving a significant 5.13% improvement on average. Notably, JointMatch
delivers impressive results even in the extremely-scarce-label setting,
obtaining 86% accuracy on AG News with only 5 labels per class. We make our
code available at https://github.com/HenryPengZou/JointMatch.