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The Event Causality Identification Shared Task of CASE 2022 involved twosubtasks working on the Causal News Corpus. Subtask 1 required participants topredict if a sentence contains a causal relation or not. This is a supervisedbinary classification task. Subtask 2 required participants to identify theCause, Effect and Signal spans per causal sentence. This could be seen as asupervised sequence labeling task. For both subtasks, participants uploadedtheir predictions for a held-out test set, and ranking was done based on binaryF1 and macro F1 scores for Subtask 1 and 2, respectively. This paper summarizesthe work of the 17 teams that submitted their results to our competition and 12system description papers that were received. The best F1 scores achieved forSubtask 1 and 2 were 86.19% and 54.15%, respectively. All the top-performingapproaches involved pre-trained language models fine-tuned to the targetedtask. We further discuss these approaches and analyze errors acrossparticipants' systems in this paper.

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