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DARE: Data Augmented Relation Extraction with GPT-2

2020-04-06 14:38:36
Yannis Papanikolaou, Andrea Pierleoni
arXiv_CL
               
Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2004.13845

PDF

https://arxiv.org/pdf/2004.13845.pdf

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