The effective application of contrastive learning technology in natural
language processing tasks shows the superiority of contrastive learning in text
analysis tasks. How to construct positive and negative samples correctly and
reasonably is the core challenge of contrastive learning. Since it is difficult
to construct contrastive objects in multi-label multi-classification tasks,
there are few contrastive losses for multi-label multi-classification text
classification. In this paper, we propose five contrastive losses for
multi-label multi-classification tasks. They are Strict Contrastive Loss (SCL),
Intra-label Contrastive Loss (ICL), Jaccard Similarity Contrastive Loss (JSCL),
and Jaccard Similarity Probability Contrastive Loss (JSPCL) and Stepwise Label
Contrastive Loss (SLCL). We explore the effectiveness of contrastive learning
for multi-label multi-classification tasks under different strategies, and
provide a set of baseline methods for contrastive learning techniques on
multi-label classification tasks. We also perform an interpretability analysis
of our approach to show how different contrastive learning methods play their
roles. The experimental results in this paper demonstrate that our proposed
contrastive losses can bring some improvement for multi-label
multi-classification tasks. Our work reveal how to "appropriately" change the
contrastive way of contrastive learning is the key idea to improve the
adaptability of contrastive learning in multi-label multi-classification tasks.

本文提出了五个用于多标签多分类任务的对比损失，探索了对比学习在不同策略下用于多标签多分类任务的有效性，并为对比学习技术在多标签分类任务上提供了一组基准方法。本文的实验结果表明，我们提出的对比损失可以为多标签多分类任务带来一定的提升。我们的研究揭示，在多标签多分类任务中，如何 “适当地” 改变对比学习的对比方式是提高其适应性的关键思想。