The Causal News Corpus: Annotating Causal Relations in Event Sentences from News (2022.lrec-1)
Copied to clipboard
Fiona Anting Tan, Ali Hürriyetoğlu, Tommaso Caselli, Nelleke Oostdijk, Tadashi Nomoto, Hansi Hettiarachchi, Iqra Ameer, Onur Uca, Farhana Ferdousi Liza, Tiancheng Hu
| Challenge: | Existing annotation guidelines for event causality focus on only explicit relations or clauses. |
| Approach: | They propose an annotation schema for event causality that addresses these concerns . they annotated 3,559 event sentences from protest event news with labels on whether it contains causal relations or not. |
| Outcome: | The proposed annotation schema for event causality addresses these concerns . it performs well with 81.20% F1 score on test set and 83.46% in 5-folds cross-validation . |
Similar Papers
Modeling Document-level Causal Structures for Event Causal Relation Identification (N19-1)
Copied to clipboard
| 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 . |
| Approach: | They propose to identify all event causal relations in a document, both within a sentence and across sentences. |
| Outcome: | The proposed model improves the performance of causal relation identification . it shows that the model can be used to identify cross-sentence causal relations . |
PolitiCause: An Annotation Scheme and Corpus for Causality in Political Texts (2024.lrec-main)
Copied to clipboard
| Challenge: | PolitiCAUSE is a new corpus of political texts annotated for causality . it provides a detailed and robust annotation scheme for analyzing causal information . |
| Approach: | They propose a new corpus of political texts annotated for causality . they provide a detailed and robust annotation scheme for annotating causal information . |
| Outcome: | The proposed method achieves a moderate performance on the dataset, with a MCC score of 0.62. |
Identifying Predictive Causal Factors from News Streams (D19-1)
Copied to clipboard
| Challenge: | Existing word embedding techniques are not suited to learn relationships between words in different documents and contexts. |
| Approach: | They propose a new framework to uncover the relationship between news events and real world phenomena by measuring how word occurrence influences future occurrence. |
| Outcome: | The proposed framework outperforms existing methods in stock price prediction errors for 12 months and 4 years. |
Graph Convolutional Networks for Event Causality Identification with Rich Document-level Structures (2021.naacl-main)
Copied to clipboard
| 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 . |
| Outcome: | The proposed model achieves state-of-the-art on two benchmark datasets. |
Event Causality Is Key to Computational Story Understanding (2024.naacl-long)
Copied to clipboard
| Challenge: | Cognitive science and symbolic AI research suggest that event causality provides vital information for story understanding. |
| Approach: | They propose a method for event causality identification that leads to material improvements in story understanding. |
| Outcome: | The proposed method improves story understanding on the COPES dataset . it achieves 4.1-10.9% increase on Clip Accuracy and 4.2-13.5% increase on Sentence IoU . |
CRAB: Assessing the Strength of Causal Relationships Between Real-world Events (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing models for reasoning about events in narratives do not understand the complexity of the causal relationships of events in the narrative. |
| Approach: | They propose a Causal Reasoning Assessment Benchmark to evaluate causal understanding of events in narratives. |
| Outcome: | The proposed model performs worse when models are derived from complex causal structures than simple linear causal chains. |
A Multi-Level Benchmark for Causal Language Understanding in Social Media Discourse (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing datasets focus on explicit causality in structured text, providing limited support for detecting implicit causal expressions. |
| Approach: | They propose a dataset of Reddit posts annotated across four causal tasks . they use a binary causal classification, explicit vs. implicit causality, cause–effect span extraction and causal gist generation to bridge causal detection and reasoning over informal discourse. |
| Outcome: | The proposed dataset analyzes 10,120 Reddit posts discussing public health related to the COVID-19 pandemic. |
Event Causality Recognition Exploiting Multiple Annotators’ Judgments and Background Knowledge (D19-1)
Copied to clipboard
| Challenge: | Existing methods for recognizing event causality written in web texts ignore each annotator's independent judgments, but we exploit each anorator''s judgments to predict the majority vote labels. |
| Approach: | They propose to grasp each annotator's policy by training multiple classifiers that predict the labels given by a single annotators and combine the outputs to predict the final labels determined by majority vote. |
| Outcome: | The proposed methods grasp each annotator's policy and combine the outputs to predict the final labels determined by majority vote. |
Knowledge-Enriched Event Causality Identification via Latent Structure Induction Networks (2021.acl-long)
Copied to clipboard
| 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. |
Improving Event Causality Identification via Self-Supervised Representation Learning on External Causal Statement (2021.findings-acl)
Copied to clipboard
| 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. |
| Outcome: | The proposed method significantly outperforms existing methods on EventSto-ryLine and Causal-TimeBank (+2.0 and +3.4 points on F1 value respectively). |