Document-Level Event Argument Extraction by Conditional Generation (2021.naacl-main)
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| Challenge: | Existing event extraction models have been limited to the sentence level . this formulation signifies a misalignment between the information seeking behavior and the informative seeking behavior. |
| Approach: | They propose a document-level neural event argument extraction model by formulating the task as conditional generation following event templates. |
| Outcome: | The proposed model achieves 7.6% F1 and 5.7% F1 over the best baseline on the document-level event extraction dataset WikiEvents and 9.3% F1 on the informative argument extraction task. |
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| Challenge: | Event argument extraction (EAE) has been well studied at the sentence level but under-explored at the document level. |
| Approach: | They propose a Few-Shot Document-Level Event Argument Extraction benchmark to capture event arguments that actually spread across sentences in documents. |
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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. |
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Generation-Augmented and Embedding Fusion in Document-Level Event Argument Extraction (2025.coling-main)
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| Challenge: | Document-level event argument extraction is a crucial task that aims to extract arguments from the entire document, beyond sentence-level analysis. |
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Document-Level Event Argument Extraction With a Chain Reasoning Paradigm (2023.acl-long)
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| Challenge: | Document-level event argument extraction aims to identify event arguments beyond sentence level, where a significant challenge is to model long-range dependencies. |
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ArgGen: Prompting Text Generation Models for Document-Level Event-Argument Aggregation (2022.findings-aacl)
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| Challenge: | Existing discourse-level information extraction tasks are extractive in nature, but extracting information from larger bodies of discourse-like documents requires more natural language understanding and reasoning capabilities. |
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Beyond Single-Event Extraction: Towards Efficient Document-Level Multi-Event Argument Extraction (2024.findings-acl)
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Wanlong Liu, Li Zhou, DingYi Zeng, Yichen Xiao, Shaohuan Cheng, Chen Zhang, Grandee Lee, Malu Zhang, Wenyu Chen
| Challenge: | mainstream event argument extraction methods process each event in isolation, resulting in inefficient inference and ignoring correlations among multiple events. |
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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. |
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Asking and Answering Questions to Extract Event-Argument Structures (2024.lrec-main)
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| Challenge: | Traditionally, corpora are limited to arguments within the same sentence, and inter-sentential arguments are more challenging and have received less attention. |
| Approach: | They propose a question-answering approach to extract document-level event-argument structures by automating questions for each argument type an event may have. |
| Outcome: | The proposed model outperforms previous models and is especially beneficial to extract arguments that appear in different sentences than the event trigger. |
REGen: A Reliable Evaluation Framework for Generative Event Argument Extraction (2025.findings-emnlp)
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| Challenge: | Existing work evaluates event argument extraction with exact match (EM), where predicted arguments must align exactly with annotated spans. |
| Approach: | They propose a Reliable Evaluation framework for Generative event argument extraction that combines exact, relaxed, and LLM-based matching to better align with human judgment. |
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Document-Level Event Argument Extraction via Optimal Transport (2022.findings-acl)
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| Challenge: | Prior work on event-level EAE models ignore syntactic structures for documents . prior work on EE is restricted to sentence-level setting where event triggers and arguments are assumed to appear in the same sentences. |
| Approach: | They propose to employ Optimal Transport to induce structures of documents based on sentence-level syntactic structures and tailored to EAE task. |
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