| 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. |
| Outcome: | The proposed method outperforms the state-of-the-art (SOTA) baselines on the M2E2 benchmark. |
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Kate Sanders, Reno Kriz, David Etter, Hannah Recknor, Alexander Martin, Cameron Carpenter, Jingyang Lin, Benjamin Van Durme
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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 . |
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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. |
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Retrieving Multimodal Information for Augmented Generation: A Survey (2023.findings-emnlp)
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Ruochen Zhao, Hailin Chen, Weishi Wang, Fangkai Jiao, Xuan Long Do, Chengwei Qin, Bosheng Ding, Xiaobao Guo, Minzhi Li, Xingxuan Li, Shafiq Joty
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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. |
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Three Stream Based Multi-level Event Contrastive Learning for Text-Video Event Extraction (2023.emnlp-main)
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| Challenge: | Existing methods for event extraction ignore motion representations in videos and are misguided by background noise. |
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LC4EE: LLMs as Good Corrector for Event Extraction (2024.findings-acl)
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| Challenge: | Event extraction (EE) is a critical task in natural language processing, yet deploying a practical EE system remains challenging. |
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Can We Edit Multimodal Large Language Models? (2023.emnlp-main)
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Towards Event Extraction with Massive Types: LLM-based Collaborative Annotation and Partitioning Extraction (2025.emnlp-main)
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| Challenge: | Event Extraction (EE) is a long-standing target, but lacks an efficient and effective annotation framework to construct the corresponding datasets. |
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Emancipating Event Extraction from the Constraints of Long-Tailed Distribution Data Utilizing Large Language Models (2024.lrec-main)
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| Challenge: | Existing methods for EE depend on manual annotations, which are expensive and scarce. |
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