Deeper Task-Specificity Improves Joint Entity and Relation Extraction
Table 1: Optimal hyperparameters used for final training on the ADE and CoNLL04 datasets.
['Hyperparameter', 'ADE', 'CoNLL04']
In evaluating NER performance on these datasets, a predicted entity is only considered a true positive if both the entity’s span and span type are correctly predicted. In evaluating RE performance, we follow previous work in adopting a strict evaluation method wherein a predicted relation is only considered correct if the spans corresponding to the two arguments of this relation and the entity types of these spans are also predicted correctly. We experimented with LSTMs and GRUs for all BiRNN layers in the model and experimented with using 1−3 shared BiRNN layers and 0−3 task-specific BiRNN layers for each task. The fact that the optimal number of task-specific layers differed between the two datasets demonstrates the value of taking the number of shared and task-specific layers to be a hyperparameter of our model architecture. We suspect that this is due to the limited number of relations and entities in the ADE dataset. For most examples in this dataset, it is sufficient to correctly identify a single Drug entity, a single Adverse-Effect entity, and an Adverse-Effect relation between the two entities. Thus, the NER and RE tasks for this dataset are more closely related than they are in the case of the CoNLL04 dataset. Intuitively, cases in which the NER and RE problems can be solved by relying on more shared information should require fewer task-specific layers.