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    <abstract>The Financial Document Causality Detection shared task (FinCausal) is a competition organized within the Financial Narrative Processing (FNP) workshop series. It aims to identify the causal relationship between a question and its answer in a given financial context. The dataset is built from real annual reports drafted by Spanish IBEX 35 companies and several UK companies. The task includes two subtasks, one in English and one in Spanish. It is formulated as an Extractive Question-Answering (EQA) task in which, given a context (C) and a question (Q), participants must extract the verbatim answer span (A). The 2026 edition introduces several changes to increase task difficulty, including the reformulation of 10% of the questions to require deeper reasoning and a stronger emphasis on multi-step causal chains with three or more elements, achieved by removing overly simple cases and adding 500 new complex fragments per language. Another innovation is the adoption of an LLM-as-a-judge metric on a 1–5 scale, based on a rubric designed to align better with human preferences than Semantic Answer Similarity (SAS) and Exact Match (EM). This edition was hosted as part of the LREC conference in Palma de Mallorca, Spain.</abstract>
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%0 Conference Proceedings
%T The Financial Document Causality Detection Shared Task (FinCausal 2026)
%A Moreno-Sandoval, Antonio
%A Porta, Jordi
%A Torterolo Orta, Yanco Amor
%A Stanescu, Alexia
%A Chatzi, Melina
%A Roseti, Sofía
%Y El-Haj, Mo
%Y Moreno Sandoval, Antonio
%Y Garcia-Serrano, Ana
%Y Chen, Chung-Chi
%Y Rayson, Paul
%Y Torterolo Orta, Yanco Amor
%Y Martinez, Paloma
%Y Porta, Jordi
%S The 7th Financial Narrative Processing Workshop
%D 2026
%8 May
%I European Language Resources Association (ELRA)
%C Palma de Mallorca, Spain
%F moreno-sandoval-etal-2026-financial
%X The Financial Document Causality Detection shared task (FinCausal) is a competition organized within the Financial Narrative Processing (FNP) workshop series. It aims to identify the causal relationship between a question and its answer in a given financial context. The dataset is built from real annual reports drafted by Spanish IBEX 35 companies and several UK companies. The task includes two subtasks, one in English and one in Spanish. It is formulated as an Extractive Question-Answering (EQA) task in which, given a context (C) and a question (Q), participants must extract the verbatim answer span (A). The 2026 edition introduces several changes to increase task difficulty, including the reformulation of 10% of the questions to require deeper reasoning and a stronger emphasis on multi-step causal chains with three or more elements, achieved by removing overly simple cases and adding 500 new complex fragments per language. Another innovation is the adoption of an LLM-as-a-judge metric on a 1–5 scale, based on a rubric designed to align better with human preferences than Semantic Answer Similarity (SAS) and Exact Match (EM). This edition was hosted as part of the LREC conference in Palma de Mallorca, Spain.
%R 10.63317/4zb72h45pvk2
%U https://aclanthology.org/2026.fnp-1.11/
%U https://doi.org/10.63317/4zb72h45pvk2
%P 114-124

Markdown (Informal)

[The Financial Document Causality Detection Shared Task (FinCausal 2026)](https://aclanthology.org/2026.fnp-1.11/) (Moreno-Sandoval et al., FNP 2026)

• The Financial Document Causality Detection Shared Task (FinCausal 2026) (Moreno-Sandoval et al., FNP 2026)

ACL

• Antonio Moreno-Sandoval, Jordi Porta, Yanco Amor Torterolo Orta, Alexia Stanescu, Melina Chatzi, and Sofía Roseti. 2026. The Financial Document Causality Detection Shared Task (FinCausal 2026). In The 7th Financial Narrative Processing Workshop, pages 114–124, Palma de Mallorca, Spain. European Language Resources Association (ELRA).