Improving Relation Extraction by Pre-trained Language Representations
Table 6: SemEval single-model test set performance with all entity mentions masked by an unknown (UNK) token. † marks results reported in the corresponding papers. Due to the small test set size, we report the mean and standard deviation across 5 randomly initialized runs.
['System', 'P', 'R', 'F1']
The result suggests that pre-trained language representations improve our model’s ability to generalize beyond the mention level when predicting the relation between two previously unseen entities.