DARE: Data Augmented Relation Extraction with GPT-2
arXiv:2004.13845
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.
References in corpus (5)
- BioBERT: a pre-trained biomedical language representation model for biomedical text mining
- The Effectiveness of Data Augmentation in Image Classification using Deep Learning
- GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification
- Simple BERT Models for Relation Extraction and Semantic Role Labeling
- LAMOL: LAnguage MOdeling for Lifelong Language Learning
Cited by in corpus (7)
- Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey
- Towards Zero-Label Language Learning
- On Data Augmentation for Extreme Multi-label Classification
- LearnDA: Learnable Knowledge-Guided Data Augmentation for Event Causality Identification
- EGFI: Drug-Drug Interaction Extraction and Generation with Fusion of Enriched Entity and Sentence Information
- Neural Data-to-Text Generation with LM-based Text Augmentation
- CubeTR: Learning to Solve The Rubiks Cube Using Transformers