Enhancing Label Correlation Feedback in Multi-Label Text Classification via Multi-Task Learning

In multi-label text classification (MLTC), each given document is associated with a set of correlated labels.To capture label correlations, previous classifier-chain and sequenceto-sequence models transform MLTC to a sequence prediction task.However, they tend to suffer from label order dependency, label combination over-fitting and error propagation problems.To address these problems, we introduce a novel approach with multi-task learning to enhance label correlation feedback.We first utilize a joint embedding (JE) mechanism to obtain the text and label representation simultaneously.In MLTC task, a document-label cross attention (CA) mechanism is adopted to generate a more discriminative document representation.Furthermore, we propose two auxiliary label co-occurrence prediction tasks to enhance label correlation learning: 1) Pairwise Label Co-occurrence Prediction (PLCP), and 2) Conditional Label Co-occurrence Prediction (CLCP).Experimental results on AAPD and RCV1-V2 datasets show that our method outperforms competitive baselines by a large margin.We analyze low-frequency label performance, label dependency, label combination diversity and coverage speed to show the effectiveness of our proposed method on label correlation learning.