Papers by Ruili Pu

2 papers
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.
Enhancing Event Causality Identification with Event Causal Label and Event Pair Interaction Graph (2023.findings-acl)

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Challenge: Existing methods for event causality identification (ECI) do not consider event causal label information and interaction information between event pairs.
Approach: They propose a framework to enrich the representation of event pairs by introducing the event causal label information and the interaction information between event pairs.
Outcome: The proposed framework outperforms state-of-the-art methods on two benchmark datasets.

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