  Multi-label text classification is a challenging task because it requires
capturing label dependencies. It becomes even more challenging when class
distribution is long-tailed. Resampling and re-weighting are common approaches
used for addressing the class imbalance problem, however, they are not
effective when there is label dependency besides class imbalance because they
result in oversampling of common labels. Here, we introduce the application of
balancing loss functions for multi-label text classification. We perform
experiments on a general domain dataset with 90 labels (Reuters-21578) and a
domain-specific dataset from PubMed with 18211 labels. We find that a
distribution-balanced loss function, which inherently addresses both the class
imbalance and label linkage problems, outperforms commonly used loss functions.
Distribution balancing methods have been successfully used in the image
recognition field. Here, we show their effectiveness in natural language
processing. Source code is available at
https://github.com/Roche/BalancedLossNLP.
