Advancing Event Causality Identification via Heuristic Semantic Dependency Inquiry Network (2024.emnlp-main)
Copied to clipboard
| 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. |
| Outcome: | Extensive experiments show that SemDI surpasses state-of-the-art methods on three widely used benchmarks. |
Similar Papers
Semantic Structure Enhanced Event Causality Identification (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for Event Causality Identification (ECI) capture implicit associations between events, which are difficult because they lack the ability to understand the associations between two events. |
| Approach: | They propose a model that captures the implicit associations between two events and integrates the event-centric structure information into a GNN-based event aggregator. |
| Outcome: | The proposed model improves on three widely used datasets showing that it integrates event-centric and event-associated semantic elements and captures event associations. |
Enhancing Event Causality Identification with LLM Knowledge and Concept-Level Event Relations (2025.coling-main)
Copied to clipboard
| 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. |
| Outcome: | The proposed method outperforms previous state-of-the-art methods on both benchmarks, EventStoryLine and Causal-TimeBank. |
SERE: Structural Example Retrieval for Enhancing LLMs in Event Causality Identification (2026.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have demonstrated strong performance across various NLP tasks, but their effectiveness in ECI remains limited due to biases in causal reasoning. |
| Approach: | They propose a structural example retrieval framework that leverages LLMs’ few-shot learning capabilities to help LLM models in ECI. |
| Outcome: | The proposed framework leverages LLMs’ few-shot learning capabilities to guide LLM models in causal reasoning, mitigating bias and improving accuracy. |
Event Causality Extraction with Event Argument Correlations (2022.coling-1)
Copied to clipboard
| Challenge: | Event Causality Identification (ECI) ignores crucial event structure and cause-effect component information, making it struggle for downstream applications. |
| Approach: | They propose a task to extract event causality pairs with their structured event information from plain text. |
| Outcome: | The proposed method captures the intra- and inter-event argument correlations for ECE and provides several future directions. |
Enhancing Event Causality Identification with Event Causal Label and Event Pair Interaction Graph (2023.findings-acl)
Copied to clipboard
| 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. |
Enhancing Event Causality Identification with Counterfactual Reasoning (2023.acl-short)
Copied to clipboard
| 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. |
| Outcome: | The proposed method eliminates biases in inference on two datasets. |
Event Causality Identification with Synthetic Control (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to event causality identification have primarily utilized linguistic patterns and multi-hop relational inference, risking false causality . |
| Approach: | They propose to use the Rubin Causal Model to identify event causality by generating a twin from existing corpora. |
| Outcome: | The proposed method can identify causal relations more robustly than previous methods, including GPT-4, which is demonstrated on a causality benchmark, COPES-hard. |
Hierarchical Selection of Important Context for Generative Event Causality Identification with Optimal Transports (2024.lrec-main)
Copied to clipboard
| 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. |
| Outcome: | The proposed framework can predict causal relation between two events in text without external tools. |
CHEER: Centrality-aware High-order Event Reasoning Network for Document-level Event Causality Identification (2023.acl-long)
Copied to clipboard
| 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. |
| Outcome: | The proposed model performs high-order reasoning while considering event centrality. |
Dr.ECI: Infusing Large Language Models with Causal Knowledge for Decomposed Reasoning in Event Causality Identification (2025.coling-main)
Copied to clipboard
| 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. |
| Outcome: | The proposed framework improves the state-of-the-art performance of LLMs for event causality identification (ECI) tasks compared with baselines based on LLM and supervised training. |