LED: Label Correlation Enhanced Decoder for Multi-Label Text Classification

Abstract

Multi-label text classification, which aims to predict the relevant labels for each given document, is one of the fundamental tasks of natural language processing. Recent studies have utilized Transformer, which embeds texts and class labels into a joint space to capture the label correlation. However, existing methods tend to take up extra input length and ignore the significance of taxonomic hierarchy. For this reason, we introduce a label correlation enhanced decoder (LED) for multi-label text classification. LED predicts the presence or absence of class labels in parallel with label representation and captures label correlation through multi-task learning. In addition, we propose a hierarchy-aware mask to capture the hierarchical dependency between labels. Comprehensive experiments on four benchmark datasets show that LED outperforms the state-of-the-art baselines. Detailed analysis validates the effectiveness of our proposed method.