The Financial Document Causality Detection Shared Task (FinCausal 2025): Dataset

<p>The Financial Document Causality Detection Shared Task (FinCausal 2025) aims to improve causality identification in the financial domain through textual data.
This shared task focuses on determining causality associated with both events and quantified facts. In this task, a cause can be the justification of a statement or the reason explaining an outcome. Therefore, it is a relation detection task.
The main difference compared to the 2023 edition is that the task is framed as a Question Answering (QA) problem. The question is posed in an abstractive manner, while the predicted answer must be extractive. Additionally, the Semantic Answer Similarity (SAS) metric has been introduced.</p>
<p>Participants, given the context and the abstractive question, must extract the literal answer from the context that responds to that question. The questions seek causal-type relationships, either causes or effects.</p>
<p>The task dataset has been extracted from a corpus of Spanish financial annual reports from 2014 to 2018. Participants are provided with a CSV file containing the following fields: ID; Text; Question; Answer.</p>
<p>The standard way to participate is to fine-tune a model using the data annotated by linguists (including Inter-Annotator Agreement, IAA), and then use the fine-tuned model to predict the "ANSWER" field in the test set.</p>
<p>This publication refers to the dataset used in the competition.</p>
<p>This is a dataset from the FinCausal 2025 competition. It is designed for participants to use it to fine-tune their models and complete the task with the highest possible similarity to the gold standard, according to the established metrics.</p>
<p>It consists of texts annotated by linguists, where a context, an abstractive question, and its corresponding extractive answer—which addresses the causal nature of the question—are provided.</p>
<p>There are two versions available: one in English and one in Spanish.</p>