Papers with boundary-aware augmentation

1 papers
Boundary-Aware LLM Augmentation for Low-Resource Event Argument Extraction (2026.eacl-long)

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Challenge: Event argument extraction (EAE) is a crucial task in information extraction but its performance heavily depends on expensive annotated data.
Approach: They investigate argument replacement, adjunction rewriting, their combination, and annotation generation using four LLM-based augmentation strategies.
Outcome: The proposed methods improve performance over boundary-agnostic methods and provide detailed analysis of quality from multiple perspectives.

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