Papers with document-level event argument extraction
Event Pattern-Instance Graph: A Multi-Round Role Representation Learning Strategy for Document-Level Event Argument Extraction (2025.findings-acl)
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| Challenge: | Existing role-based span selection strategies ignore interrelations between events . authors propose a multi-round role representation learning strategy for document-level event argument extraction . |
| Approach: | They propose a pattern-instance graph to capture role semantics embedded in various associations . they also propose re-inventing the role representations learned from previous analyzed documents . |
| Outcome: | The proposed model captures role semantics embedded in various associations . iteratively updates representations of role nodes and edges to enrich their semantic information . the model improves prediction performance in subsequent rounds of span selection . |
Document-Level Event Argument Extraction by Leveraging Redundant Information and Closed Boundary Loss (2022.naacl-main)
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| Challenge: | Document-level event argument extraction is a crucial subtask of event extraction. |
| Approach: | They propose to use redundant event information to extract multiple arguments from a document . they propose a loss function to classify Universum class by their open decision boundary . |
| Outcome: | The proposed model outperforms the previous state-of-the-art models by 3.35% in F1-score. |
Generating Uncontextualized and Contextualized Questions for Document-Level Event Argument Extraction (2024.naacl-long)
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| Challenge: | Existing methods for document-level argument extraction do not require human involvement and combine uncontextualized and contextualized questions. |
| Approach: | They propose multiple question generation strategies for document-level event argument extraction that do not require human involvement and combine uncontextualized and contextualized questions. |
| Outcome: | The proposed questions do not require human involvement and are suitable for document-level argument extraction. |
Thinking about how to extract: Energizing LLMs’ emergence capabilities for document-level event argument extraction (2024.findings-acl)
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| Challenge: | Existing models for document-level event argument extraction (D-EAE) lack key feature forgetting and cross-event argument confusion. |
| Approach: | They propose a document-level event argument extraction method based on guided summarization and reasoning that leverages the emergence capabilities of large language models to highlight key event information. |
| Outcome: | The proposed method outperforms baseline models by 1.3% F1 and 1.6% F1 on WIKIEVENTS and RAMS. |
Dynamic Global Memory for Document-level Argument Extraction (2022.acl-long)
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| Challenge: | Recent work on document-level event argument extraction is restricted by sequence length constraints and ignores global context between events. |
| Approach: | They propose to construct a document memory store to extract contextual event information and leverage it to implicitly and explicitly help with decoding of arguments for later events. |
| Outcome: | The proposed framework outperforms prior methods and is more robust to adversarially annotated examples with constrained decoding design. |
LLMs Learn Task Heuristics from Demonstrations: A Heuristic-Driven Prompting Strategy for Document-Level Event Argument Extraction (2024.acl-long)
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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). |
| Approach: | They propose an explicit heuristic-driven demonstration construction approach that emphasizes task heurs in document-level event argument extraction tasks. |
| Outcome: | The proposed method outperforms existing prompting methods and few-shot supervised learning methods on document-level EAE datasets. |