  The effectiveness of contrastive learning technology in natural language
processing tasks is yet to be explored and analyzed. How to construct positive
and negative samples correctly and reasonably is the core challenge of
contrastive learning. It is even harder to discover contrastive objects in
multi-label text classification tasks. There are very few contrastive losses
proposed previously. In this paper, we investigate the problem from a different
angle by proposing five novel contrastive losses for multi-label text
classification tasks. These are Strict Contrastive Loss (SCL), Intra-label
Contrastive Loss (ICL), Jaccard Similarity Contrastive Loss (JSCL), Jaccard
Similarity Probability Contrastive Loss (JSPCL), and Stepwise Label Contrastive
Loss (SLCL). We explore the effectiveness of contrastive learning for
multi-label text classification tasks by the employment of these novel losses
and provide a set of baseline models for deploying contrastive learning
techniques on specific tasks. We further perform an interpretable analysis of
our approach to show how different components of contrastive learning losses
play their roles. The experimental results show that our proposed contrastive
losses can bring improvement to multi-label text classification tasks. Our work
also explores how contrastive learning should be adapted for multi-label text
classification tasks.
