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Data · dataset · 2025

The financial document causality detection shared task (FinCausal 2023): Dataset

Listed in e-cienciaDatos

The Financial Document Causality Detection Task (FinCausal 2023) aims at improving the causality in the financial domain trough its texts.

Description

Participants are asked to identify, in causal sentences, which elements of the sentence relate to the cause, and which relate to the effect. LLI-UAM is the organizer of the Spanish subtask.

The task dataset has been extracted from a corpus of Spanish financial annual reports from 2014 to 2018. This shared task focuses on determining causality associated with both events or quantified facts. For this task, a cause can be the justification for a statement or the reason that explains a result.

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Therefore, it is a relationship detection task. The aim is to identify, in a paragraph, the causal elements and the consequential ones. Only one causal element and one effect are expected in each paragraph.

Participants are provided with a sample of paragraphs, labelled through inter-annotator agreement. This publication consists of the dataset of the shared task. It is a dataset from the FinCausal 2023 competition.

It is designed for participants to use the dataset for fine-tuning their models in order to complete the task with the highest possible similarity to the gold standard. It consists of texts annotated by linguists, highlighting the cause and effect present in a paragraph with a financial theme.

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Economics & Finance
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e-cienciaDatosdoi:10.21950/2JOAZJ12 d agoJSON v1
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