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The current state-of-the-art model HiAGM for hierarchical text classificationhas two limitations. First, it correlates each text sample with all labels inthe dataset which contains irrelevant information. Second, it does not considerany statistical constraint on the label representations learned by thestructure encoder, while constraints for representation learning are proved tobe helpful in previous work. In this paper, we propose HTCInfoMax to addressthese issues by introducing information maximization which includes twomodules: text-label mutual information maximization and label prior matching.The first module can model the interaction between each text sample and itsground truth labels explicitly which filters out irrelevant information. Thesecond one encourages the structure encoder to learn better representationswith desired characteristics for all labels which can better handle labelimbalance in hierarchical text classification. Experimental results on twobenchmark datasets demonstrate the effectiveness of the proposed HTCInfoMax.

HTCInfoMax: A Global Model for Hierarchical Text Classification via Information Maximization