论文标题
使用多个注释决策的因果关系检测
Causality Detection using Multiple Annotation Decisions
论文作者
论文摘要
本文描述了已提交给第五次研讨会的工作，内容涉及从文本中自动提取社会政治事件的挑战和应用（案例2022）。这项工作与共享任务3的子任务1相关，旨在检测抗议新闻语料库中的因果关系。作者使用了不同的大型语言模型，并具有自定义的跨凝结损失函数来利用注释信息。实验表明，基于BERT的精制跨透明拷贝的表现优于其他实验，在因果新闻语料库数据集上获得了0.8501的F1分数。
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.
