Targeted Augmentation for Low-Resource Event Extraction
This addresses the problem of information scarcity in low-resource event extraction for NLP researchers, though it appears incremental as it builds on existing data augmentation methods.
The paper tackles low-resource event extraction by introducing a targeted augmentation and back validation paradigm, which produces augmented examples with improved diversity, polarity, accuracy, and coherence, as demonstrated in extensive experiments.
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.