  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 sequence-to-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.
