Semantic-Unit-Based Dilated Convolution for Multi-Label Text Classification
Table 2: Performance on the RCV1-V2 test set. HL, P, R, and F1 denote hamming loss, micro-precision, micro-recall and micro-F1, respectively (p<0.05).
['[BOLD] Models', '[BOLD] HL(-)', '[BOLD] P(+)', '[BOLD] R(+)', '[BOLD] F1(+)']
From the results of the conventional baselines, it can be found that the classical methods for multi-label text classification still own competitiveness compared with the machine-learning-based and even deep-learning-based methods, instead of the Seq2Seq-based models. Regarding the Seq2Seq model, both the S2S and the S2S+Attn achieve improvements on the dataset, compared with the baselines above. However, as mentioned previously, the attention mechanism does not play a significant role in the Seq2Seq model for multi-label text classification. By contrast, our proposed mechanism, which is label-classification-oriented, can take both the information of semantic units and that of word units into consideration. Our proposed model achieves the best performance in the evaluation of Hamming loss and micro-F1 score, which reduces 9.8% of Hamming loss and improves 1.3% of micro-F1 score, in comparison with the S2S+Attn.