Event Causality Identification - Shared Task 3, CASE 2023
Published in 6th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE), 2023
Fiona Anting Tan, Hansi Hettiarachchi, Ali Hürriyetoglu, Nelleke Oostdijk, Onur Uca, Surendrabikram Thapa, and Farhana Ferdousi Liza. 2023. Event Causality Identification with Causal News Corpus - Shared Task 3, CASE 2023. In Proceedings of the 6th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE), Varna, Bulgaria (Hybrid). Association for Computational Linguistics. https://aclanthology.org/2023.case-1.19/
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Abstract
The Event Causality Identification Shared Task of CASE 2023 is the second iteration of a shared task centered around the Causal News Corpus. Two subtasks were involved: In Subtask 1, participants were challenged to predict if a sentence contains a causal relation or not. In Subtask 2, participants were challenged to identify the Cause, Effect, and Signal spans given an input causal sentence. 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 includes an overview of the work of the ten teams that submitted their results to our competition and the six system description papers that were received. The highest F1 scores achieved for Subtask 1 and 2 were 84.66% and 72.79%, respectively.