  Multi-label text classification (MLTC) is an attractive and challenging task
in natural language processing (NLP). Compared with single-label text
classification, MLTC has a wider range of applications in practice. In this
paper, we propose a heterogeneous graph convolutional network model to solve
the MLTC problem by modeling tokens and labels as nodes in a heterogeneous
graph. In this way, we are able to take into account multiple relationships
including token-level relationships. Besides, the model allows a good
explainability as the token-label edges are exposed. We evaluate our method on
three real-world datasets and the experimental results show that it achieves
significant improvements and outperforms state-of-the-art comparison methods.
