  Multi-label classification (MLC) assigns multiple labels to each sample.
Prior studies show that MLC can be transformed to a sequence prediction problem
with a recurrent neural network (RNN) decoder to model the label dependency.
However, training a RNN decoder requires a predefined order of labels, which is
not directly available in the MLC specification. Besides, RNN thus trained
tends to overfit the label combinations in the training set and have difficulty
generating unseen label sequences. In this paper, we propose a new framework
for MLC which does not rely on a predefined label order and thus alleviates
exposure bias. The experimental results on three multi-label classification
benchmark datasets show that our method outperforms competitive baselines by a
large margin. We also find the proposed approach has a higher probability of
generating label combinations not seen during training than the baseline
models. The result shows that the proposed approach has better generalization
capability.
