Causality Detection using Multiple Annotation Decision

• URL: http://arxiv.org/abs/2210.14852v1
• Date: Wed, 26 Oct 2022 16:50:10 GMT
• Title: Causality Detection using Multiple Annotation Decision
• Authors: Quynh Anh Nguyen, Arka Mitra
• Abstract summary: The work is associated with Subtask 1 of Shared Task 3 that aims to detect causality in protest news corpus. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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