Virtual Data Augmentation

2000년 도입 · 논문 4편에서 사용

Virtual Data Augmentation, or VDA, is a framework for robustly fine-tuning pre-trained language model. Based on the original token embeddings, a multinomial mixture for augmenting virtual data is constructed, where a masked language model guarantees the semantic relevance and the Gaussian noise provides the augmentation diversity. Furthermore, a regularized training strategy is proposed to balance the two aspects.

출처: Virtual Data Augmentation: A Robust and General Framework for Fine-tuning Pre-trained Models

소개 논문: Virtual Data Augmentation: A Robust and General Framework for Fine-tuning Pre-trained Models