Rule-Guided Extraction: A Hierarchical Rule Optimization Framework for Document-Level Event Argument Extraction (2025.findings-emnlp)
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| Challenge: | Document-level event argument extraction (EAE) is a critical task in natural language processing. |
| Approach: | They propose an LLM-driven HiErarchical Rule Optimization framework that iteratively generates and selects optimal hierarchical rules. |
| Outcome: | The proposed framework outperforms few-shot supervised methods and outperformed state-of-the-art prompting baselines. |
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| Challenge: | Existing models for document-level event argument extraction (D-EAE) lack key feature forgetting and cross-event argument confusion. |
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| Challenge: | Structural extraction of events within discourse is critical for event-centric understanding . document-level EAE focuses on arguments that are scattered across an entire document . ULTRA is a hierarchical framework that extracts event arguments more cost-effectively . |
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| Challenge: | Event argument extraction (EAE) has been well studied at the sentence level but under-explored at the document level. |
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| Challenge: | In-context learning (ICL) is an emerging ability of large-scale labeled data for document-level event argument extraction (EAE). |
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Prompt for Extraction? PAIE: Prompting Argument Interaction for Event Argument Extraction (2022.acl-long)
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| Challenge: | Using a prompt-based model, we find that event argument extraction is efficient and generalized well to few-shot settings. |
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