论文标题
与因果新闻语料库的事件因果关系识别 - 共享任务3，案例2022
Event Causality Identification with Causal News Corpus -- Shared Task 3, CASE 2022
论文作者
论文摘要
事件因果关系标识共享案件2022的任务涉及在因果新闻语料库上工作的两个子任务。子任务1要求参与者预测句子是否包含因果关系。这是一项监督的二进制分类任务。子任务2要求参与者确定每个因果句子的原因，效果和信号跨度。这可以看作是监督序列标签任务。对于这两个子任务，参与者都上传了对持有测试集的预测，并且分别基于子任务1和2的二进制F1和宏F1分数进行排名。本文总结了17个团队的工作，这些团队将其结果提交给我们的竞争对手，并收到了12个系统描述论文。子任务1和2的最佳F1分别分别为86.19％和54.15％。所有表现最佳的方法都涉及对目标任务进行微调的预训练的语言模型。我们进一步讨论了这些方法，并在本文中分析了参与者系统之间的错误。
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.
