ERGO: Event Relational Graph Transformer for Document-level Event Causality Identification (2022.coling-1)
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| Challenge: | Existing methods to identify event-event causal relations in a document are noisy and require heuristic rules or external tools. |
| Approach: | They propose a document-level event-event causality identification framework that uses heuristic rules to design edges between events. |
| Outcome: | The proposed framework outperforms existing state-of-the-art methods on two benchmark datasets. |
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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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CHEER: Centrality-aware High-order Event Reasoning Network for Document-level Event Causality Identification (2023.acl-long)
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| Challenge: | Recent studies focus on building a document-level graph for cross-sentence reasoning, but ignore important causal structures. |
| Approach: | They propose a document-level event causality identification model which annotates central events and incorporates event centrality information into the reasoning network. |
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Discriminative Reasoning with Sparse Event Representation for Document-level Event-Event Relation Extraction (2023.acl-long)
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| Challenge: | Document-level Event Causality Identification (DECI) is a sentence-level task that requires long-text understanding. |
| Approach: | They propose a document-level event causality identification model (SENDIR) that uses sparse attention to capture long-distance dependence. |
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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. |
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Suggest-Verify-Revise: A Three-Stage Document-Level Event Causality Identification with Narrative Consistency (2026.acl-long)
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| Challenge: | Existing methods for document-level Event Causality Identification rely on local semantic similarity for independent event-pair discrimination . Existing approaches ignore the influence of the overall narrative backbone in the propagation of causal dependencies and the role differentiation of events within multi-cause/multi-effect structures. |
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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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Modeling Document-level Causal Structures for Event Causal Relation Identification (N19-1)
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| Challenge: | a study aims to identify all the event causal relations in a document, both within a sentence and across sentences . main challenges for achieving comprehensive causal relation identification are sparse among all possible event pairs . few causal relations are explicitly stated, especially for identifying cross-sentence causal relations . |
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GLiM: Integrating Graph Transformer and LLM for Document-Level Biomedical Relation Extraction with Incomplete Labeling (2025.findings-acl)
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Hao Fang, Yuejie Zhang, Rui Feng, Yingwen Wang, Qing Wang, Wen He, Xiaobo Zhang, Tao Zhang, Shang Gao
| Challenge: | Document-level relation extraction (DocRE) solves problems of document quality . number of entities and entity-pair relations increases, causing incomplete annotations . |
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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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Knowledge-Enriched Event Causality Identification via Latent Structure Induction Networks (2021.acl-long)
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| Challenge: | Existing methods for identifying causal relations of events are limited . Existing approaches cannot handle well the problem, especially in the condition of lacking training data. |
| Approach: | They propose a Latent Structure Induction Network to integrate external structural knowledge into a causality reasoning task. |
| Outcome: | The proposed approach outperforms existing state-of-the-art methods on two widely used datasets. |