| Challenge: | et al., 2015) only consider local arguments in the same sentence of the event trigger. |
| Approach: | They propose to decompose the implicit event argument detection task into two sub-problems . they propose to use argument head-word detection and head-to-span expansion to reduce the number of candidates. |
| Outcome: | The proposed model achieves better performance than a strong sequence labeling baseline. |
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Multi-Sentence Argument Linking (2020.acl-main)
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| Challenge: | Existing datasets for cross-sentence linking are small, resulting in a lack of a model for argument linking. |
| Approach: | They propose a document-level model for finding argument spans that fill an event’s roles by combining semantic role labeling and coreference resolution. |
| Outcome: | The proposed model is able to connect arguments in sentence-level role labeling and coreference resolution on 9,124 annotated events across 139 types. |
Explicit, Implicit, and Scattered: Revisiting Event Extraction to Capture Complex Arguments (2024.emnlp-main)
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| Challenge: | Existing work on event-specific argument extraction is limited to contiguous spans of text . Existing approaches to event-centric information extraction are limited to explicit arguments . |
| Approach: | They propose two key argument types that cannot be modeled by existing EE frameworks . implicit and scattered arguments are crucial to elicit full breadth of information required for proper event modeling. |
| Outcome: | The proposed dataset includes 7,464 argument annotations from online health discourse. |
Trigger is Not Sufficient: Exploiting Frame-aware Knowledge for Implicit Event Argument Extraction (2021.acl-long)
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| Challenge: | Existing methods to extract event arguments focus on learning pair-wise information between arguments and the given trigger. |
| Approach: | They propose a framework to extract event-related arguments from a given event frame-level scope. |
| Outcome: | The proposed method achieves state-of-the-art on the RAMS dataset. |
Few-Shot Document-Level Event Argument Extraction (2023.acl-long)
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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. |
| Outcome: | The proposed task is very challenging with low performance and limited learning process . argument extraction depends on context from multiple sentences and learning process limited to very few examples . |
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. |
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. |
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. |
| Approach: | They propose a multi-event argument argument extraction model which extracts arguments from all events simultaneously. |
| Outcome: | The proposed model performs better on four public datasets while saving time. |
A Two-Stream AMR-enhanced Model for Document-level Event Argument Extraction (2022.naacl-main)
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| Challenge: | Existing studies aim at extracting event arguments from a single sentence . document-level event extraction still remains under-explored . |
| Approach: | They propose a two-stream abstract meaning representation enhanced extraction model to extract event arguments from an entire document. |
| Outcome: | The proposed model outperforms state-of-the-art in extracting event arguments from documents by 2.54 F1 and 5.13 F1 on public RAMS and WikiEvents datasets. |
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. |
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. |