A Deep Reinforced Sequence-to-Set Model for Multi-Label Text Classification
Table 4: Performance on the AAPD test set. Detailed explanations of symbols can be found in Table 3.
['[BOLD] Models', '[BOLD] HL(-)', '[BOLD] P(+)', '[BOLD] R(+)', '[BOLD] F1(+)']
Similar to the results on the RCV1-V2 test set, our proposed methods still outperform all baselines by a large margin. This further shows that our models can not only have significant advantages over other traditional methods, but also reduce the dependence of the model on the label order and alleviate the wrong penalty, leading the performance much better than the traditional SeqSeq model.