  Multi-label text classification (MLTC) is one of the key tasks in natural
language processing. It aims to assign multiple target labels to one document.
Due to the uneven popularity of labels, the number of documents per label
follows a long-tailed distribution in most cases. It is much more challenging
to learn classifiers for data-scarce tail labels than for data-rich head
labels. The main reason is that head labels usually have sufficient
information, e.g., a large intra-class diversity, while tail labels do not. In
response, we propose a Pairwise Instance Relation Augmentation Network (PIRAN)
to augment tailed-label documents for balancing tail labels and head labels.
PIRAN consists of a relation collector and an instance generator. The former
aims to extract the document pairwise relations from head labels. Taking these
relations as perturbations, the latter tries to generate new document instances
in high-level feature space around the limited given tailed-label instances.
Meanwhile, two regularizers (diversity and consistency) are designed to
constrain the generation process. The consistency-regularizer encourages the
variance of tail labels to be close to head labels and further balances the
whole datasets. And diversity-regularizer makes sure the generated instances
have diversity and avoids generating redundant instances. Extensive
experimental results on three benchmark datasets demonstrate that PIRAN
consistently outperforms the SOTA methods, and dramatically improves the
performance of tail labels.
