Event Causality Identification - Shared Task 3, CASE 2023 (2023)

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.

## introduction a causal relation represents a semantic relationship between a cause argument and an effect argument, where the occurrence of the cause triggers the occurrence of the effect @xcite . the extraction of causal information from text holds significant implications for downstream applications in natural language processing (nlp), like for summarization and prediction @xcite @xcite ), question answering @xcite @xcite , inference and understanding @xcite . given the limited availability of data for causal text mining @xcite @xcite , in 2022, the causal news corpus (cnc) was created @xcite . 1 we also introduced a shared task to promote modelling for two causal text mining tasks: (1) causal event classification and (2) cause-effect-signal span detection @xcite . this paper describes the second iteration of this shared task. in this iteration, some parts of our data have updated labels and for subtask 2, much more annotated data is provided. the remainder of the paper is organized as follows: section 2 describes the dataset and its annotations. section 3 formally introduces the two subtasks for the shared task. section 4 describes the evaluation metrics and competition set-up. next, section 5 summarizes the methods used by par ## dataset our shared task uses version 2 (v2) of the causal news corpus @xcite , which is based on the corpora released in the scope of @xcite . ## shared task description the task is comprised of two subtasks related to event causality identification: (1) causal event classification and (2) cause-effect-signal span detection. the objective of each subtask is described below in sections 3.1 and 3.2. the 2023 edition is the second iteration of this shared task which was first introduced in 2022 @xcite . the shared task is re-launched to work on the larger and revised cnc-v2 discussed in the earlier section. additionally, for subtask 2, the traditional evaluation metrics (p, r and f1) were updated to use fairer evaluation calculations, discussed in section 4.1. ## conclusion in conclusion, our shared task investigated two important tasks in causal text mining, namely: (1) causal event classification, and (2) cause-effect-signal span detection. our shared task attracted 23 registered participants and 10 active participants. based on the six description papers received, some novel methods that exceeded our initial baseline were proposed. the best f1 scores achieved for subtask 1 and 2 were 84.66% and 72.79% respectively.