Reframing Responsibility: Framing-Aware Event Causality Identification (2026.acl-long)
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| Challenge: | Causal explanations in political narratives are often framed and contested. |
| Approach: | They propose a framing-aware extension of ECI that models causal explanations as structured claims including responsibility targets, evaluative frams, source type, and epistemic modality. |
| Outcome: | The proposed model enables quantitative analysis of divergent causal attribution across narratives. |
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| Challenge: | Existing models for document-level Event Causality Identification (ECI) are limited to intra-sentence contexts where event mention pairs are presented in the same sentences. |
| Approach: | They propose a deep learning model that accepts inter-sentence event mention pairs . they use interaction graphs to capture relevant connections between important objects . |
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Advancing Event Causality Identification via Heuristic Semantic Dependency Inquiry Network (2024.emnlp-main)
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| Challenge: | Existing methods for ECI rely on causal features and external knowledge, but these methods fail in two dimensions: causal features between events in texts often lack explicit clues and external information may introduce bias. |
| Approach: | They propose a simple and effective Semantic Dependency Inquiry Network for ECI that captures semantic dependencies within the context using a unified encoder and generates a fill-in token based on comprehensive context understanding. |
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Enhancing Event Causality Identification with Counterfactual Reasoning (2023.acl-short)
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| Challenge: | Existing methods for event causality identification (ECI) focus on mining potential causal signals, but causal signals are ambiguous, which may lead to the context-keywords bias and the event-pairs bias. |
| Approach: | They propose a method that explicitly estimates the influence of context keywords and event pairs in training to eliminate biases in inference. |
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Improving Event Causality Identification via Self-Supervised Representation Learning on External Causal Statement (2021.findings-acl)
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| Challenge: | Existing methods for event causality identification (ECI) rely on labeled data, but the scale of annotated datasets is limited. |
| Approach: | They propose a self-supervised framework to learn context-specific causal patterns from external causal statements and adopt a contrastive transfer strategy to incorporate the learned context- specific causal patterns into the target ECI model. |
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Event Causality Identification with Synthetic Control (2024.emnlp-main)
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| Challenge: | Existing approaches to event causality identification have primarily utilized linguistic patterns and multi-hop relational inference, risking false causality . |
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Hierarchical Selection of Important Context for Generative Event Causality Identification with Optimal Transports (2024.lrec-main)
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| Challenge: | Existing methods for Event Causality Identification (ECI) rely on external toolkits or human annotation to obtain training signals. |
| Approach: | They propose a generative framework that leverages Optimal Transport to automatically select the most important sentences and words from full documents. |
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Mitigating Causal Bias in LLMs via Potential Outcomes Framework and Actual Causality Theory (2026.findings-eacl)
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| Challenge: | Large Language Models exhibit significant causal hallucination, but evaluation of their document-level ECI performance is lacking. |
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Enhancing Event Causality Identification with LLM Knowledge and Concept-Level Event Relations (2025.coling-main)
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| Challenge: | Existing methods to identify causal relationships between events often overlook the dependencies between similar events. |
| Approach: | They propose an ECI method enhanced by LLM Knowledge and Concept-Level Event Relations (LKCER) the method constructs a conceptual-level heterogeneous event graph by leveraging local contextual information of related event mentions. |
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Event Causality Extraction with Event Argument Correlations (2022.coling-1)
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| Challenge: | Event Causality Identification (ECI) ignores crucial event structure and cause-effect component information, making it struggle for downstream applications. |
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Dr.ECI: Infusing Large Language Models with Causal Knowledge for Decomposed Reasoning in Event Causality Identification (2025.coling-main)
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| Challenge: | Existing solutions lack generalizability to unseen domains, underscoring the urgent need for generalization capabilities in the field of ECI. |
| Approach: | They propose a multi-agent Decomposed reasoning framework for Event Causality Identification that incorporates specialized agents such as Causal Explorer and Mediator Detector. |
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