Exploring Contrastive Learning for Long-Tailed Multi-Label Text Classification

arXiv:2404.08720v1 Announce Type: new
Abstract: Learning an effective representation in multi-label text classification (MLTC) is a significant challenge in NLP. This challenge arises from the inherent complexity of the task, which is shaped by two key factors: the intricate connections between labels and the widespread long-tailed distribution of the data. To overcome this issue, one potential approach involves integrating supervised contrastive learning with classical supervised loss functions. Although contrastive learning has shown remarkable performance in multi-class classification, its impact in …

abstract arxiv challenge classification complexity cs.cl cs.ir cs.lg data distribution key labels nlp representation text text classification type