Imposing Label-Relational Inductive Biasfor Extremely Fine-Grained Entity Typing
Table 5: Results on OntoNotes. Upper rows show the results of baselines that explicitly use the hierarchical type structures.
['[BOLD] Model', '[BOLD] Accuracy', '[BOLD] Macro-F1', '[BOLD] Micro-F1']
To better understand the requirements for applying our model, we further evaluate on the OntoNotes dataset. Here we do not apply the proposed mention-context matching module as this dataset does not include any pronoun entities. To obtain more reliable co-occurrence statistics, we use the augmented training data released by Choi et al. However, since the training set is still much smaller than that of the Ultra-Fine dataset, the derived co-occurrence statistics are relatively noisy and might introduce undesired bias. We thus add an additional residual connection to our graph convolution layer, which allows the model to selectively use co-occurrence statistics. However, compared to Ultra-Fine, the margin of the improvement is smaller. In view of the key differences of these two datasets, we highlight two key requirements for our proposed model to offer substantial improvements. First, there should be a large-scale training set so that the derived co-occurrence statistics can reasonably reflect the true label correlations. Second, the samples themselves should also have higher label multiplicity. In fact, most of the samples in OntoNotes only have 1 or 2 labels. This property actually alleviates the need for models to capture label dependencies.