Semantic-Unit-Based Dilated Convolution for Multi-Label Text Classification
Table 5: Performance of the hierarchical model and our model on the RCV1-V2 test set. Hier refers to hierarchical model, and the subsequent number refers to the length of sentence (word) for sentence-level representations (p<0.05).
['[BOLD] Models', '[BOLD] HL(-)', '[BOLD] P(+)', '[BOLD] R(+)', '[BOLD] F1(+)']
Moreover, in order to alleviate the influence of the deterministic sentence boundary, we compare the performance of different hierarchical models with different boundaries, which sets the boundaries at the end of every 5, 10, 15 and 20 words respectively. This shows that high-level representations can contribute to the performance of the Seq2Seq model on the multi-label text classification task. Furthermore, as these performances are no better than that of our proposed model, it can reflect that the learnable high-level representations can contribute more than deterministic sentence-level representations, as it can be more flexible to represent information of diverse levels, instead of fixed phrase or sentence level.