@inproceedings{moreno-sandoval-etal-2025-financial,
title = "The Financial Document Causality Detection Shared Task ({F}in{C}ausal 2025)",
author = "Moreno Sandoval, Antonio and
Carbajo Coronado, Blanca and
Porta Zamorano, Jordi and
Torterolo Orta, Yanco Amor and
Samy, Doaa",
editor = "Chen, Chung-Chi and
Moreno-Sandoval, Antonio and
Huang, Jimin and
Xie, Qianqian and
Ananiadou, Sophia and
Chen, Hsin-Hsi",
booktitle = "Proceedings of the Joint Workshop of the 9th Financial Technology and Natural Language Processing (FinNLP), the 6th Financial Narrative Processing (FNP), and the 1st Workshop on Large Language Models for Finance and Legal (LLMFinLegal)",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.finnlp-1.21/",
pages = "214--221",
abstract = "We present the Financial Document Causality Detection Task (FinCausal 2025), a multilingual challenge to identify causal relationships within financial texts. This task comprises English and Spanish subtasks, with datasets compiled from British and Spanish annual reports. Participants were tasked with identifying and generating answers to questions about causes or effects within specific text segments. The dataset combines extractive and generative question-answering (QA) methods, with abstractly formulated questions and directly extracted answers from the text. Systems performance is evaluated using exact matching and semantic similarity metrics. The challenge attracted submissions from 10 teams for the English subtask and 10 teams for the Spanish subtask. FinCausal 2025 is part of the 6th Financial Narrative Processing Workshop (FNP 2025), hosted at COLING 2025 in Abu Dhabi."
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%0 Conference Proceedings
%T The Financial Document Causality Detection Shared Task (FinCausal 2025)
%A Moreno Sandoval, Antonio
%A Carbajo Coronado, Blanca
%A Porta Zamorano, Jordi
%A Torterolo Orta, Yanco Amor
%A Samy, Doaa
%Y Chen, Chung-Chi
%Y Moreno-Sandoval, Antonio
%Y Huang, Jimin
%Y Xie, Qianqian
%Y Ananiadou, Sophia
%Y Chen, Hsin-Hsi
%S Proceedings of the Joint Workshop of the 9th Financial Technology and Natural Language Processing (FinNLP), the 6th Financial Narrative Processing (FNP), and the 1st Workshop on Large Language Models for Finance and Legal (LLMFinLegal)
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F moreno-sandoval-etal-2025-financial
%X We present the Financial Document Causality Detection Task (FinCausal 2025), a multilingual challenge to identify causal relationships within financial texts. This task comprises English and Spanish subtasks, with datasets compiled from British and Spanish annual reports. Participants were tasked with identifying and generating answers to questions about causes or effects within specific text segments. The dataset combines extractive and generative question-answering (QA) methods, with abstractly formulated questions and directly extracted answers from the text. Systems performance is evaluated using exact matching and semantic similarity metrics. The challenge attracted submissions from 10 teams for the English subtask and 10 teams for the Spanish subtask. FinCausal 2025 is part of the 6th Financial Narrative Processing Workshop (FNP 2025), hosted at COLING 2025 in Abu Dhabi.
%U https://aclanthology.org/2025.finnlp-1.21/
%P 214-221
Markdown (Informal)
[The Financial Document Causality Detection Shared Task (FinCausal 2025)](https://aclanthology.org/2025.finnlp-1.21/) (Moreno Sandoval et al., FinNLP 2025)
ACL
- Antonio Moreno Sandoval, Blanca Carbajo Coronado, Jordi Porta Zamorano, Yanco Amor Torterolo Orta, and Doaa Samy. 2025. The Financial Document Causality Detection Shared Task (FinCausal 2025). In Proceedings of the Joint Workshop of the 9th Financial Technology and Natural Language Processing (FinNLP), the 6th Financial Narrative Processing (FNP), and the 1st Workshop on Large Language Models for Finance and Legal (LLMFinLegal), pages 214–221, Abu Dhabi, UAE. Association for Computational Linguistics.