Label Correlation Based Graph Convolutional Network for Multi-label Text Classification

Multi-label text classification aims to assign a set of most relevant labels to a given document. To build such a classifier, apart from demanding an efficient document representation, capturing label information for classification performance improvement is still challenging. In this paper, we propose a novel model based on a graph convolutional network to model label correlation. To do that, we design a correlation matrix from labels in a data-driven way. The learned label correlations are then fused with fine-grained document information extracted by a RoBERTa-based subnet for classification. Furthermore, we introduce a simple mechanism to make the label correlation matrix more effective in propagating information among label nodes. We first normalize the correlation matrix to deal with the highly skewed problem and then filter noisy edges to alleviate the long-tailed distribution problem. Evaluation results show that our model achieves competitive results compared to existing state-of-the-art methods. Ablation studies are also conducted to explore the proposed model's behaviors.