  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 label-interpretable 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
better interpretability for predicted labels as the token-label edges are
exposed. We evaluate our method on four real-world datasets and it achieves
competitive scores against selected baseline methods. Specifically, this model
achieves a gain of 0.14 on the F1 score in the small label set MLTC, and 0.07
in the large label set scenario.
