Correlation-Guided Representation for Multi-Label Text Classification

Abstract

Multi-label text classification is an essential task in natural language processing. Existing multi-label classification models generally consider labels as categorical variables and ignore the exploitation of label semantics. In this paper, we view the task as a correlation-guided text representation problem: an attention-based two-step framework is proposed to integrate text information and label semantics by jointly learning words and labels in the same space. In this way, we aim to capture high-order label-label correlations as well as context-label correlations. Specifically, the proposed approach works by learning token-level representations of words and labels globally through a multi-layer Transformer and constructing an attention vector through word-label correlation matrix to generate the text representation. It ensures that relevant words receive higher weights than irrelevant words and thus directly optimizes the classification performance. Extensive experiments over benchmark multi-label datasets clearly validate the effectiveness of the proposed approach, and further analysis demonstrates that it is competitive in both predicting low-frequency labels and convergence speed.