Learning to Learn and Predict: A Meta-Learning Approach for Multi-Label Classification
Table 2: The experimental results on fine-grained entity typing datasets. Acc.: Accuracy.
['[BOLD] Datasets  [BOLD] Metrics', '[BOLD] FIGER  [BOLD] Acc.', '[BOLD] FIGER  [BOLD] Macro', '[BOLD] FIGER  [BOLD] Micro', '[BOLD] OntoNotes  [BOLD] Acc.', '[BOLD] OntoNotes  [BOLD] Macro', '[BOLD] OntoNotes  [BOLD] Micro', '[BOLD] BBN  [BOLD] Acc.', '[BOLD] BBN  [BOLD] Macro', '[BOLD] BBN  [BOLD] Micro']
S5SS0SSS0Px3 Results From the results, we observe that: (1) Our meta-learning method can outperform all the baselines in all three metrics, which indicates the capability of our methods in improving the multi-label classification. (2) The SOTA model trained with learned training policy outperforms the one with hierarchy-aware training policy. It is because that our learned training policy can capture not only explicit hierarchical dependencies of labels, but also model other implicit label dependencies. (3) The three different prediction policies can improve the performance of the SOTA classifier. The results are consistent with previous researches that choosing a good prediction policy is an effective way to improve the performance Fan and Lin (4) Compared with OntoNotes and BBN datasets, the FIGER show a relatively less improvement when applying these policies. The reason is that the test set of FIGER is not fine-grained enough (e.g., over 38% of entities are only annotated with /person and no more fine-grained labels) Xin et al.