Event Extraction with Generative Adversarial Imitation Learning
Table 5: Performance between extractors trained with different schema in sequence labeling.
['Tasks Metric', 'Trigger Labeling P', 'Trigger Labeling R', 'Trigger Labeling F1', 'Role Labeling P', 'Role Labeling R', 'Role Labeling F1']
The results drop drastically. Based on this observation, we can conclude that the extractor benefits from the parameter and embedding shared from training process with entity extraction, even thought it is not required to extract entities during test phase.