Home > Research > Publications & Outputs > Event Causality Identification - Shared Task 3,...

Links

View graph of relations

Event Causality Identification - Shared Task 3, CASE 2023

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Published
  • Fiona Anting Tan
  • Hansi Hettiarachchi
  • Ali Hürriyetoğlu
  • Nelleke Oostdijk
  • Onur Uca
  • Surendrabikram Thapa
  • Farhana Ferdousi Liza
Close
Publication date7/09/2023
Host publicationProceedings of the 6th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text
Place of PublicationShoumen, Bulgaria
PublisherINCOMA Ltd
Pages144-150
Number of pages7
ISBN (electronic)9789544520892
<mark>Original language</mark>English

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.