The Event Causality Identification Shared Task of CASE 2022 involved two subtasks working on the Causal News Corpus. Subtask 1 required participants to predict if a sentence contains a causal relation or not. This is a supervised binary classification task. Subtask 2 required participants to identify the Cause, Effect and Signal spans per causal sentence. This could be seen as a supervised sequence labeling task. For both subtasks, participants uploaded their predictions for a held-out test set, and ranking was done based on binary F1 and macro F1 scores for Subtask 1 and 2, respectively. This paper summarizes the work of the 17 teams that submitted their results to our competition and 12 system description papers that were received. The best F1 scores achieved for Subtask 1 and 2 were 86.19% and 54.15%, respectively. All the top-performing approaches involved pre-trained language models fine-tuned to the targeted task. We further discuss these approaches and analyze errors across participants' systems in this paper.
翻译:CASE 2022 事件因果关系识别共同任务涉及两个子任务,分别涉及Causal News Corpus。 Subtask 1 要求参与者预测一个句子是否包含因果关系。 这是一个受监督的二进制分类任务。 Subtask 2 要求参与者确定每个因果句的起因、 效果和信号范围。 这可被视为一个受监督的序列标签任务。 对于这两个子任务,参与者上传了他们关于搁置测试成套方案的预测, 分级分别基于二进F1和子任务1和2的宏F1分。 本文总结了17个向我们的竞争提交结果的团队的工作和收到的12份系统描述文件。 Subtask 1 和 2 获得的最佳F1分数分别为86.19%和54.15%。 所有最优秀的方法都涉及经过预先训练的语言模型,要对目标任务进行微调。 我们进一步讨论了这些方法,并分析了本文中参与者系统中各系统的错误。