  Addressing the challenge of low-resource information extraction remains an
ongoing issue due to the inherent information scarcity within limited training
examples. Existing data augmentation methods, considered potential solutions,
struggle to strike a balance between weak augmentation (e.g., synonym
augmentation) and drastic augmentation (e.g., conditional generation without
proper guidance). This paper introduces a novel paradigm that employs targeted
augmentation and back validation to produce augmented examples with enhanced
diversity, polarity, accuracy, and coherence. Extensive experimental results
demonstrate the effectiveness of the proposed paradigm. Furthermore, identified
limitations are discussed, shedding light on areas for future improvement.
