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The Financial Document Causality Detection Shared Task (FinCausal 2023)

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Publication date1/02/2024
Host publication2023 IEEE International Conference on Big Data
EditorsJingrui He, Themis Palpanas, Xiaohua Hu, Alfredo Cuzzocrea, Dejing Dou, Dominik Slezak, Wei Wang, Aleksandra Gruca, Jerry Chun-Wei Lin, Rakesh Agrawal
Place of PublicationLos Alamitos, CA, USA
PublisherIEEE Computer Society Press
Pages2855-2860
Number of pages6
ISBN (electronic)9798350324457
<mark>Original language</mark>English

Publication series

NameProceedings - 2023 IEEE International Conference on Big Data, BigData 2023

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.