Papers with M2E2

2 papers
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.
Outcome: The proposed method outperforms the state-of-the-art (SOTA) baselines on the M2E2 benchmark.
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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