MMUTF: Multimodal Multimedia Event Argument Extraction with Unified Template Filling (2024.findings-emnlp)
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| Challenge: | Recent MEE methods focus on weak alignment strategies and data augmentation with simple classification models. |
| Approach: | They propose a unified template filling model that connects textual and visual modalities via textual prompts. |
| Outcome: | The proposed model surpasses the current SOTA on textual EAE by +7% F1 and performs generally better than the second-best systems for multimedia EAE. |
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| Challenge: | We propose a new task to extract events and their arguments from multimedia documents . traditional methods target text, images or videos, but multimedia content is distributed via multimedia . |
| Approach: | They propose a method that encodes structured representations of semantic information from textual and visual data into a common embedding space. |
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Separation and Fusion: A Novel Multiple Token Linking Model for Event Argument Extraction (2024.naacl-long)
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Jing Xu, Dandan Song, Siu Hui, Zhijing Wu, Meihuizi Jia, Hao Wang, Yanru Zhou, Changzhi Zhou, Ziyi Yang
| Challenge: | Existing methods for event argument extraction (EAE) lack cross-event information and require longer role sequences . et al. (2017): outperforms state-of-the-art methods for EE. |
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Multimedia Event Extraction with LLM Knowledge Editing (2025.emnlp-main)
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| Challenge: | Existing multimodal event extraction methods focus on weakly aligning features from wellpretrained unimodal encoders, resulting in redundant feature perception. |
| Approach: | They propose a multimodal event extraction strategy with a redundant feature selection mechanism that enhances event understanding ability of multimodal large language models. |
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Multi-Document Event Extraction Using Large and Small Language Models (2025.emnlp-main)
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| Challenge: | Existing approaches to multi-document event extraction have limited attention . despite its practical significance, this task has inherent challenges . |
| Approach: | They propose a collaborative framework that integrates large language models for multi-step reasoning and fine-tuned small language models to handle key subtasks. |
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On Event Individuation for Document-Level Information Extraction (2023.findings-emnlp)
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| Challenge: | a bomb exploded in a restaurant in Lima, and a second device was deactivated by the police . |
| Approach: | They argue that the task demands definitive answers to thorny questions of *event individuation* they argue that even human experts disagree on the task . |
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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. |
| Approach: | They propose a conditional text generation approach which generates consolidated event-arguments at a document-level with minimal loss of information. |
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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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Fusion meets Function: The Adaptive Selection-Generation Approach in Event Argument Extraction (2025.coling-main)
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| Challenge: | Event Argument Extraction is a critical subtask of Event Extraction, focused on identifying event arguments within text. |
| Approach: | They propose a Fusion Selection-Generation-Based Approach that merges selective and generative methods to enhance argument extraction accuracy. |
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ULTRA: Unleash LLMs’ Potential for Event Argument Extraction through Hierarchical Modeling and Pair-wise Self-Refinement (2024.findings-acl)
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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 . |
| Approach: | They propose a hierarchical framework that extracts event arguments more cost-effectively . ULTRA sequentially reads text chunks of a document to generate a candidate argument set . they propose to use a supervised model to find the exact boundary of an argument . |
| Outcome: | The proposed framework outperforms strong models and ChatGPT by 9.8% when evaluated by Exact Match (EM). |
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. |
| Approach: | They propose a novel approach to document-level event argument extraction that integrates predefined templates and generative language models into a foundational embedding derived from a classification model. |
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