DARE: Data Augmented Relation Extraction with GPT-2
Figure 1: DARE vs balanced bagging(BB) for different sizes of positive samples on CDR dataset. Both methods employ ensembles of BERT RE classifiers.
['Dataset', 'BB', 'DARE']
In all cases, our approach has a steady, significant advantage over the balanced bagging baseline, their difference reaching up to 11 F1 score points when only few positives (≤250) are available. As we add more samples, the differences start to smooth out as expected. These results clearly illustrate that DARE can boost the predictive power of a classifier when dealing with few positive samples, by cheaply generating training data of arbitrary sizes.