The Financial Document Causality Detection Shared Task (FinCausal 2023)

Moreno-Sandoval, A. and Porta-Zamorano, J. and Carbajo-Coronado, B. and Samy, D. and Mariko, D. and El-Haj, M. (2024) The Financial Document Causality Detection Shared Task (FinCausal 2023). In: 2023 IEEE International Conference on Big Data :. Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023 . IEEE Computer Society Press, Los Alamitos, CA, USA, pp. 2855-2860. ISBN 9798350324457

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Abstract

We introduce the FinCausal 2023 Shared Task on Causality Detection in Financial Documents and the corresponding FinCausal dataset. This paper also provides insights into the participating systems and their outcomes. The primary objective of this task is to identify whether an object, event or sequence of events can be considered the cause of a preceding event (the effect). This year, we presented two subtasks, one in English and another in Spanish. In both subtasks, participants were tasked with pinpointing, within causal sentences, the elements that pertained to the cause and those that related to the effect. We received system runs from five teams for the English subtask and three teams for the Spanish subtask. FinCausal 2023 is affiliated with the 5th Financial Narrative Processing Workshop (FNP 2023), hosted at IEEE BigData 2023 in Sorrento, Italy.