Event Causality Identification
13 papers with code • 0 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Event Causality Identification
COLD: Causal Reasoning in Closed Daily Activities
Most implemented papers
IDIAPers @ Causal News Corpus 2022: Efficient Causal Relation Identification Through a Prompt-based Few-shot Approach
In this paper, we describe our participation in the subtask 1 of CASE-2022, Event Causality Identification with Casual News Corpus.
IDIAPers @ Causal News Corpus 2022: Extracting Cause-Effect-Signal Triplets via Pre-trained Autoregressive Language Model
In this paper, we describe our shared task submissions for Subtask 2 in CASE-2022, Event Causality Identification with Casual News Corpus.
Event Causality Extraction with Event Argument Correlations
cuishiyao96/ece
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COLING 2022
Event Causality Identification (ECI), which aims to detect whether a causality relation exists between two given textual events, is an important task for event causality understanding.
Event Causality Is Key to Computational Story Understanding
insundaycathy/event-causality-extraction
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Cognitive science and symbolic AI research suggest that event causality provides vital information for story understanding.
BoschAI @ Causal News Corpus 2023: Robust Cause-Effect Span Extraction using Multi-Layer Sequence Tagging and Data Augmentation
boschresearch/boschai-cnc-shared-task-ranlp2023
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Understanding causality is a core aspect of intelligence.
In-context Contrastive Learning for Event Causality Identification
Motivated from such considerations, this paper proposes an In-Context Contrastive Learning (ICCL) model that utilizes contrastive learning to enhance the effectiveness of both positive and negative demonstrations.
Identifying while Learning for Document Event Causality Identification
LchengC/iLIF
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Event Causality Identification (ECI) aims to detect whether there exists a causal relation between two events in a document.
Weak Reward Model Transforms Generative Models into Robust Causal Event Extraction Systems
The inherent ambiguity of cause and effect boundaries poses a challenge in evaluating causal event extraction tasks.
Advancing Event Causality Identification via Heuristic Semantic Dependency Inquiry Network
hrlics/semdi
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However, these approaches fall short in two dimensions: (1) causal features between events in a text often lack explicit clues, and (2) external knowledge may introduce bias, while specific problems require tailored analyses.
COLD: Causal reasOning in cLosed Daily activities
Large Language Models (LLMs) have shown state-of-the-art performance in a variety of tasks, including arithmetic and reasoning; however, to gauge the intellectual capabilities of LLMs, causal reasoning has become a reliable proxy for validating a general understanding of the mechanics and intricacies of the world similar to humans.
