mlsanityData to 7 Oct 2026

DARE: Data Augmented Relation Extraction with GPT-2

arXiv 2004.13845 ↗First posted 6 Apr 2020cs.CL cs.LG stat.MLcited by 43

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Abstract (arXiv)

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.

Key facts. Cited by 43 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Apr 2020.

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Citing papers per year: 2021: 13; 2022: 7; 2023: 11; 2024: 9; 2025: 2; 2026: 1.

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