Dual Representation Space Optimization for Multi-Label Text Classification

Binjie Sun, Xuejie Zhang, Jin Wang, Xiaobing Zhou

Abstract: Multi-label text classification (MLTC) presents significant challenges due to the need for accurate document representations and the effective modeling of complex label dependencies. Existing methods either underutilize label semantics for representation generation or face challenges in fully capturing inter-label dependencies using contrastive learning, often leading to suboptimal label predictions. To address these issues, we propose a novel end-to-end framework, Dual Representation Space Optimization (DRSO), for MLTC. DRSO tackles these challenges through two key components: a semantic-aware network that refines document representations by leveraging label semantics and an adaptive multi-label contrastive learning mechanism that captures inter-label dependencies to optimize label distributions in the prediction space. Extensive experiments on benchmark datasets demonstrate that DRSO outperforms state-of-the-art methods, showcasing its effectiveness in enhancing both representation quality and label prediction accuracy1.

External IDs:dblp:conf/ijcnn/SunZWZ25