  The current state-of-the-art model HiAGM for hierarchical text classification
has two limitations. First, it correlates each text sample with all labels in
the dataset which contains irrelevant information. Second, it does not consider
any statistical constraint on the label representations learned by the
structure encoder, while constraints for representation learning are proved to
be helpful in previous work. In this paper, we propose HTCInfoMax to address
these issues by introducing information maximization which includes two
modules: text-label mutual information maximization and label prior matching.
The first module can model the interaction between each text sample and its
ground truth labels explicitly which filters out irrelevant information. The
second one encourages the structure encoder to learn better representations
with desired characteristics for all labels which can better handle label
imbalance in hierarchical text classification. Experimental results on two
benchmark datasets demonstrate the effectiveness of the proposed HTCInfoMax.
