Resolving Event Coreference with Supervised Representation Learning and Clustering-Oriented Regularization
Table 2: Model comparison based on validation set B3 accuracy with optimized τ cluster-similarity threshold. For CORE+CCE+Lemma (indicated as CORE+CCE+L) we tuned to δ=0.89; for Lemma-δ we tuned to δ=0.67.
['[BOLD] Model', '[ITALIC] λ1', '[ITALIC] λ2', 'B3', '[ITALIC] τ']
Interestingly, we observe that CORE+CCE performs slightly better with λ2=0; i.e., without repulsive regularization. This suggests that enforcing representation similarity is more important than enforcing division, although we cannot conclusively state that repulsive regularization would not be useful for other tasks. Nonetheless, for test set results we use the optimal hyperparameter configurations found during this validation-tuning step; e.g., for CORE+CCE we set λ1=2 and λ2=0.