A Deep Reinforced Sequence-to-Set Model for Multi-Label Text Classification
Table 3: Performance on the RCV1-V2 test set. HL, P, R, and F1 denote hamming loss, micro-precision, micro-recall and micro-F1, respectively. The symbol “+” indicates that the higher the value is, the better the model performs. The symbol “-” is the opposite. Seq2Set (simp.) denotes the simplified Seq2Set model.
['[BOLD] Models', '[BOLD] HL(-)', '[BOLD] P(+)', '[BOLD] R(+)', '[BOLD] F1(+)']
Results show that both of our proposed methods outperform all baselines by a large margin and the proposed Seq2Set model achieves the best performance in the main evaluation metrics. For instance, our Seq2Set model achieves a reduction of 18.60% hamming-loss and an improvement of 3.38% micro-F1 score over the most commonly used baseline BR. In addition, it also has a large margin over the traditional Seq2Seq model, which shows that the use of reinforcement learning is of great help to improve the accuracy of classification.