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.

该论文总结了 17 个团队提交的结果以及 12 篇系统描述论文，介绍了他们在 CASE 2022 的事件因果性识别共享任务的表现。最好的 F1 分数分别为 86.19％和 54.15％，所有表现最佳的方法都涉及针对目标任务进行微调的预先训练的语言模型。本文还讨论了这些方法并分析了参与者系统中的错误。

利用因果新闻语料库进行事件因果识别 -- 共享任务 3，CASE 2022

Event Causality Identification with Causal News Corpus -- Shared Task 3, CASE 2022

Despite the importance of understanding causality, corpora addressing causal
relations are limited. There is a discrepancy between existing annotation
guidelines of event causality and conventional causality corpora that focus
more on linguistics. Many guidelines restrict themselves to include only
explicit relations or clause-based arguments. Therefore, we propose an
annotation schema for event causality that addresses these concerns. We
annotated 3,559 event sentences from protest event news with labels on whether
it contains causal relations or not. Our corpus is known as the Causal News
Corpus (CNC). A neural network built upon a state-of-the-art pre-trained
language model performed well with 81.20% F1 score on test set, and 83.46% in
5-folds cross-validation. CNC is transferable across two external corpora:
CausalTimeBank (CTB) and Penn Discourse Treebank (PDTB). Leveraging each of
these external datasets for training, we achieved up to approximately 64% F1 on
the CNC test set without additional fine-tuning. CNC also served as an
effective training and pre-training dataset for the two external corpora.
Lastly, we demonstrate the difficulty of our task to the layman in a
crowd-sourced annotation exercise. Our annotated corpus is publicly available,
providing a valuable resource for causal text mining researchers.

该研究提出了一种用于事件因果关系注释的注释模式，为此构建了一个名为 Causal News Corpus（CNC）的语料库，并使用神经网络在测试集上获得了 81.20％ F1 得分，CNC 是对外部语料库可转移的，也是文本挖掘研究人员的有价值资源。