  Multi-label text classification (MLTC) aims to assign multiple labels to each
sample in the dataset. The labels usually have internal correlations. However,
traditional methods tend to ignore the correlations between labels. In order to
capture the correlations between labels, the sequence-to-sequence (Seq2Seq)
model views the MLTC task as a sequence generation problem, which achieves
excellent performance on this task. However, the Seq2Seq model is not suitable
for the MLTC task in essence. The reason is that it requires humans to
predefine the order of the output labels, while some of the output labels in
the MLTC task are essentially an unordered set rather than an ordered sequence.
This conflicts with the strict requirement of the Seq2Seq model for the label
order. In this paper, we propose a novel sequence-to-set framework utilizing
deep reinforcement learning, which not only captures the correlations between
labels, but also reduces the dependence on the label order. Extensive
experimental results show that our proposed method outperforms the competitive
baselines by a large margin.
