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

<p>The Financial Document Causality Detection Task (FinCausal 2023) aims at improving the causality in the financial domain trough its texts. 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. 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.</p>
<p>Participants are provided with a sample of paragraphs, labelled through inter-annotator agreement. This publication consists of the dataset of the shared task.</p>
<p>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.</p>

<p>金融文档因果关系检测任务（FinCausal 2023）旨在通过文本增强金融领域因果关系的识别能力。参与者需在因果句中识别句子中哪些元素与原因相关，哪些与结果相关。LLI-UAM是西班牙子任务的组织者。该任务的数据集来源于2014年至2018年的西班牙金融年报语料库。该共享任务聚焦于识别与事件或量化事实相关的因果关系。在此任务中，原因可指陈述的依据或解释结果的理由，因此这是一项关系检测任务。其目标是在段落中识别因果元素与结果元素，且每个段落仅包含一个因果元素和一个结果元素。</p>
<p>参与者将获得经标注者间一致性标注的段落样本。本出版物包含该共享任务的数据集。</p>
<p>该数据集来自FinCausal 2023竞赛，旨在供参与者用于微调模型，以完成任务并尽可能接近黄金标准。数据集由语言学家标注的文本构成，突出了金融主题段落中的原因与结果。</p>

搜集汇总

背景与挑战

背景概述

该数据集是FinCausal 2023竞赛的一部分，包含从西班牙金融年度报告中提取的段落，标注了因果关系元素（原因和结果），用于训练模型检测金融文本中的因果关系。