%0 Conference Proceedings %T LiGCN: Label-interpretable Graph Convolutional Networks for Multi-label Text Classification %A Li, Irene %A Feng, Aosong %A Wu, Hao %A Li, Tianxiao %A Suzumura, Toyotaro %A Dong, Ruihai %Y Wu, Lingfei %Y Liu, Bang %Y Mihalcea, Rada %Y Pei, Jian %Y Zhang, Yue %Y Li, Yunyao %S Proceedings of the 2nd Workshop on Deep Learning on Graphs for Natural Language Processing (DLG4NLP 2022) %D 2022 %8 July %I Association for Computational Linguistics %C Seattle, Washington %F li-etal-2022-ligcn %X 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. %R 10.18653/v1/2022.dlg4nlp-1.7 %U https://aclanthology.org/2022.dlg4nlp-1.7 %U https://doi.org/10.18653/v1/2022.dlg4nlp-1.7 %P 60-70