  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.
