Causality Detection using Multiple Annotation Decisions
This work addresses causality detection for socio-political event analysis, but it is incremental as it applies existing methods to a specific dataset.
The authors tackled causality detection in protest news by using large language models with customized cross-entropy loss functions, achieving an F1 score of 0.8501 on the Causal News Corpus dataset.
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.