Real-world Relation Extraction (RE) tasks are challenging to deal with, either due to limited training data or class imbalance issues. In this work, we present Data Augmented Relation Extraction(DARE), a simple method to augment training data by properly fine-tuning GPT-2 to generate examples for specific relation types. The generated training data is then used in combination with the gold dataset to train a BERT-based RE classifier. In a series of experiments we show the advantages of our method, which leads in improvements of up to 11 F1 score points against a strong base-line. Also, DARE achieves new state of the art in three widely used biomedical RE datasets surpassing the previous best results by 4.7 F1 points on average.
@article{arxiv.2004.13845,
title = {DARE: Data Augmented Relation Extraction with GPT-2},
author = {Yannis Papanikolaou and Andrea Pierleoni},
journal= {arXiv preprint arXiv:2004.13845},
year = {2020}
}