Abstract
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 pretrained language models fine-tuned to the targeted task. We further discuss these approaches and analyze errors across participants’ systems in this paper.
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 pretrained language models fine-tuned to the targeted task. We further discuss these approaches and analyze errors across participants’ systems in this paper.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE) |
| Publisher | Association for Computational Linguistics |
| Pages | 195 - 208 |
| Number of pages | 14 |
| ISBN (Electronic) | 978-1-959429-05-0 |
| Publication status | Published - 2022 |