Deeper Task-Specificity Improves Joint Entity and Relation Extraction
Table 3: Results from an ablation study using the CoNLL04 dataset. All models have the same number of total parameters.
['Model Type', 'Metric Type', 'NER F1', 'RE F1']
These results show that the proposed architecture benefits from the inclusion of both NER- and RE-specific layers. However, the RE task benefits much more from the inclusion of these task-specific layers than does the NER task. We take this to reflect the fact that the RE task is more difficult than the NER task for the CoNLL04 dataset, and therefore benefits the most from its own task-specific layers. This is consistent with the fact that the hyperparameter setting that performs best on the RE task is that with no NER-specific BiRNN layers, i.e. the setting that retained RE-specific BiRNN layers. In contrast, the inclusion of task-specific BiRNN layers of any kind had relatively little impact on the performance on the NER task.