EA2E: Improving Consistency with Event Awareness for Document-Level Argument Extraction (2022.findings-naacl)
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| Challenge: | Recent work on document-level event argument extraction models each individual event in isolation and therefore causes inconsistency among extracted arguments across events. |
| Approach: | They propose an event-aware argument extraction model with augmented context to improve consistency . they hypothesize that participants tend to play consistent roles across multiple events in a document . |
| Outcome: | The proposed model improves consistency and accuracy of arguments extracted from documents. |
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| Challenge: | Recent studies on event argument extraction (EAE) have not taken event co-occurrences into account. |
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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: | Existing methods for document-level event argument extraction use memory to store the results of already predicted events. |
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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
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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: | Recent studies show the effectiveness of retrieval augmentation in many generative NLP tasks. |
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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. |
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