Causality Detection using Multiple Annotation Decisions
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Abstract (arXiv)
The paper describes the work that has been submitted to the 5th workshop on Challenges and Applications of Automated Extraction of socio-political events from text (CASE 2022). The work is associated with Subtask 1 of Shared Task 3 that aims to detect causality in protest news corpus. The authors used different large language models with customized cross-entropy loss functions that exploit annotation information. The experiments showed that bert-based-uncased with refined cross-entropy outperformed the others, achieving a F1 score of 0.8501 on the Causal News Corpus dataset.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 10 of its 25 references that are in the map. First posted on arXiv 26 Oct 2022.
Builds on
10 of 25 references.
References · 10
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- 2015Statistical modality tagging from rule-based annotations and crowdsourcingcited by 6
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Citing papers per year: 2022: 1.
1 paper.