Papers with event causality
ACCESS : A Benchmark for Abstract Causal Event Discovery and Reasoning (2025.naacl-long)
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Vy Vo, Lizhen Qu, Tao Feng, Yuncheng Hua, Xiaoxi Kang, Songhai Fan, Tim Dwyer, Lay-Ki Soon, Gholamreza Haffari
| Challenge: | Existing methods for identifying event causality in NLP are limited in their scale and rely on lexical cues. |
| Approach: | They propose a benchmark for identifying abstract causality from a large-scale dataset. |
| Outcome: | The proposed benchmark can be leveraged for enhancing QA reasoning performance in LLMs. |
Event Causality Is Key to Computational Story Understanding (2024.naacl-long)
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| 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 . |
The Causal News Corpus: Annotating Causal Relations in Event Sentences from News (2022.lrec-1)
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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 . |
Event Causality Recognition Exploiting Multiple Annotators’ Judgments and Background Knowledge (D19-1)
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| 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. |