Connecting the Dots: Document-level Neural Relation Extraction with Edge-oriented Graphs
Table 9: Hyper-parameter values used in the reported experiments.
['Parameter', 'Value']
We used the development set to identify the stopping training epoch and tune the number of inference iterations. Except from these parameters, all experiments used the same hyperparameters, with a fixed initialisation seed. For the CDR dataset EoG, (Full) and (Sent) models performed best with l=8,2,4 inference steps, respectively. The chosen batchsize was equal to 2. For the GDA dataset, EoG and EoG (Full) performed best with l=16 and EoG (Sent) with l=8 inference steps. The chosen batchsize was equal to 3. For all experiments performance was measured in terms of micro precision (P), recall (R) and F1-score (F1).