TY - GEN
T1 - Event Causality Identification with Causal News Corpus - Shared Task 3, CASE 2022
AU - Tan, Fiona Anting
AU - Hettiarachchi, Hansi
AU - Hürriyetoğlu, Ali
AU - Caselli, Tommaso
AU - Uca, Onur
AU - Liza, Farhana Ferdousi
AU - Oostdijk, Nelleke
N1 - Publisher Copyright:
© 2022 Association for Computational Linguistics.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85141370000&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85141370000
T3 - CASE 2022 - 5th Workshop on Challenges and Applications of Automated Extraction of Socio-Political Events from Text, Proceedings of the Workshop
SP - 195
EP - 208
BT - CASE 2022 - 5th Workshop on Challenges and Applications of Automated Extraction of Socio-Political Events from Text, Proceedings of the Workshop
PB - Association for Computational Linguistics (ACL)
T2 - 5th Workshop on Challenges and Applications of Automated Extraction of Socio-Political Events from Text, CASE 2022
Y2 - 7 December 2022 through 8 December 2022
ER -