A systematic analysis of evaluation practices in multimedia event extraction reveals that minor methodological choices cause large performance swings and overestimation of cross-modal grounding ability.
MA VEN: A Massive General Domain Event Detection Dataset
6 Pith papers cite this work. Polarity classification is still indexing.
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EVENT5Ws is a new large-scale, manually verified open-domain event extraction dataset that benchmarks LLMs and demonstrates cross-context generalization.
Creates a Bangla event detection benchmark with clean, ASR, and corrupted text variants and finds decoder-only LLMs more robust to noise than encoder models.
ECPO is a listwise policy optimization method that couples ranking utility with span-level evidence certificate validity and a deterministic verifier reward on MAVEN-ERE and RAMS datasets.
MODEE is a multimodal system that integrates graphs with LLM embeddings to outperform prior open-domain event extraction methods on large datasets.
citing papers explorer
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Evaluation Pitfalls and Challenges in Multimedia Event Extraction
A systematic analysis of evaluation practices in multimedia event extraction reveals that minor methodological choices cause large performance swings and overestimation of cross-modal grounding ability.
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EVENT5Ws: A Large Dataset for Open-Domain Event Extraction from Documents
EVENT5Ws is a new large-scale, manually verified open-domain event extraction dataset that benchmarks LLMs and demonstrates cross-context generalization.
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Beyond Clean Text: Evaluating Encoder and Decoder Robustness for Bangla Event Detection in Noisy Text
Creates a Bangla event detection benchmark with clean, ASR, and corrupted text variants and finds decoder-only LLMs more robust to noise than encoder models.
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ECPO: Evidence-Coupled Policy Optimization for Evidence-Certified Candidate Ranking
ECPO is a listwise policy optimization method that couples ranking utility with span-level evidence certificate validity and a deterministic verifier reward on MAVEN-ERE and RAMS datasets.
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A Multimodal Text- and Graph-Based Approach for Open-Domain Event Extraction from Documents
MODEE is a multimodal system that integrates graphs with LLM embeddings to outperform prior open-domain event extraction methods on large datasets.
- EXCEEDS: Extracting Complex Events via Nugget-based Grid Modeling in Scientific Domain