Investigating Capsule Networks with Dynamic Routing for Text Classification
Table 4: Comparisons of the capability for transferring from single-label to multi-label text classification on Reuters-Multi-label and Reuters-Full datasets. For fair comparison, we use margin-loss for our model and other baselines.
['[EMPTY]', 'Reuters-Multi-label ER', 'Reuters-Multi-label Precision', 'Reuters-Multi-label Recall', 'Reuters-Multi-label F1', 'Reuters-Full ER', 'Reuters-Full Precision', 'Reuters-Full Recall', 'Reuters-Full F1']
From the results, we can observe that the capsule networks have substantial and significant improvement in terms of all four evaluation metrics over the strong baseline methods on the test sets in both Reuters-Multi-label and Reuters-Full datasets. In particular, larger improvement is achieved on Reuters-Multi-label dataset which only contains the multi-label documents in the test set. This is within our expectation since the capsule network is capable of preserving the instantiated parameters of the categories trained by single-label documents. The capsule network has much stronger transferring capability than the conventional deep neural networks. In addition, the good results on Reuters-Full also indicate that the capsule network has robust superiority over competitors on single-label documents.