DARE: Data Augmented Relation Extraction with GPT-2

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 sp...

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Hauptverfasser:	Papanikolaou, Yannis, Pierleoni, Andrea
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Computation and Language Computer Science - Learning Statistics - Machine Learning
Online-Zugang:	Volltext bestellen
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