NERO: A Neural Rule Grounding Framework forLabel-Efficient Relation Extraction
Table 4. Ablation Study of Different Training Objectives on TACRED dataset. We remove each loss term one at a time.
['Objective', 'Precision', 'Recall', '[ITALIC] F1']
1. Effect of Different Loss Functions. To show the efficacy of our proposed losses (Lclus, Lunmatched and Lrules), we conduct ablation study on loss functions by removing one loss function at a time. When removing the contrastive loss Lclus, the F1 score drops from 51.3 to 46.4, which shows the effectiveness of a trainable SRM. The Lrules loss also brings improvement, similar to including more training instances.