Papers with Event Extraction

6 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.
Literary Event Detection (P19-1)

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Challenge: a new dataset of literary events is presented to examine the nature of narratives . literature presents a number of challenges for existing systems, including complex narration .
Approach: They propose a dataset of literary events that are depicted as taking place within the imagined space of a novel.
Outcome: The proposed model achieves an F1 score of 73.9 for prestige and popularity . the best performing model achieve a score of 79.9 for prestige compared to the previous model .
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.
EDEN: A Dataset for Event Detection in Norwegian News (2024.lrec-main)

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Challenge: EDEN is the first dataset annotated with event information at the sentence level for the Norwegian language.
Approach: They propose to annotate Norwegian news text and transcribed speech using ACE event schema.
Outcome: The proposed dataset is the first annotated dataset for Norwegian, with a language-specific annotation process.
A Two-Step Approach for Implicit Event Argument Detection (2020.acl-main)

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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.
CVRH: Cross-modal Variational Role Hypergraph Network via Semantic Enhancement for Multi-modal Event Argument Extraction (2026.findings-acl)

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Challenge: Existing methods focus on weakly aligning uni-modal representations and generatively data augmentation techniques, but they ignore the potential impact of event role information on MEAE.
Approach: They propose a cross-modal variational role hypergraph network via semantic enhancement to model high-order role correlations among cross-mod arguments in multi-modal documents.
Outcome: The proposed method achieves a 6.9% improvement in F1-score on the M2E2 benchmark compared to current state-of-the-art methods.

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