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.

翻译：多标签文本分类(MLTC)在自然语言处理(NLP)中是一项有吸引力和具有挑战性的任务。与单一标签文本分类相比,MLTC在实际应用中具有更广泛的应用范围。在本文中,我们提出一个多元图形演变网络模型,通过将标牌和标签作为不同图表中的节点来解决MLTC问题。这样,我们就能够考虑多种关系,包括象征性关系。此外,随着象征性标签边缘暴露出来,该模型允许很好地解释。我们用三个真实世界数据集来评估我们的方法,实验结果显示,它取得了显著的改进,并超越了最先进的比较方法。