*FinCausal 2023: Financial Document Causality Detection*
We are glad to announce that the Training Dataset for both English and Spanish is released and ready on Codalab in this link: https://codalab.lisn.upsaclay.fr/competitions/14596
Please register on CodaLab and get to the FInCausal.2023 Competition. Under Participate, you will find the Training Datasets together with a Starting Kit to guide you through the Task.
###### *Task Description and Important Links *#######
*FinCausal-2023 Shared Task: “Financial Document Causality Detection” *is organised within the *5th Financial Narrative Processing Workshop (FNP 2023)* taking place in the 2023 IEEE International Conference on Big Data (IEEE BigData 2023) http://bigdataieee.org/BigData2023/, Sorrento, Italy, 15-18 December 2023. It is a *one-day event*. Workshop URL: https://wp.lancs.ac.uk#####cfie/fincausal2023/ https://wp.lancs.ac.uk/cfie/fincausal2023/
###### *Additional Information *#######
*Shared Task Description:*
Financial analysis needs factual data and an explanation of the variability of these data. Data state facts but need more knowledge regarding how these facts materialised. Furthermore, understanding causality is crucial in studying decision-making processes.
The *Financial Document Causality Detection Task* (FinCausal) aims at identifying elements of cause and effect in causal sentences extracted from financial documents. Its goal is to evaluate which events or chain of events can cause a financial object to be modified or an event to occur, regarding a given context. In the financial landscape, identifying cause and effect from external documents and sources is crucial to explain why a transformation occurs.
Two subtasks are organised this year. *English FinCausal subtask *and* Spanish FinCausal subtask*. This is the first year where we introduce a subtask in Spanish.
*Objective*: For both tasks, participants are asked to identify, given a causal sentence, which elements of the sentence relate to the cause, and which relate to the effect. Participants can use any method they see fit (regex, corpus linguistics, entity relationship models, deep learning methods) to identify the causes and effects.
*English FinCausal subtask*
- *Data Description: *The dataset has been sourced from various 2019 financial news articles provided by Qwam, along with additional SEC data from the Edgar Database. Additionally, we have augmented the dataset from FinCausal 2022, adding 500 new segments. Participants will be provided with a sample of text blocks extracted from financial news and already labelled. - *Scope: *The* English FinCausal subtask* focuses on detecting causes and effects when the effects are quantified. The aim is to identify, in a causal sentence or text block, the causal elements and the consequential ones. Only one causal element and one effect are expected in each segment. - *Length of Data fragments: *The* English FinCausal subtask* segments are made up of up to three sentences. - *Data format: *CSV files. Datasets for both the English and the Spanish subtasks will be presented in the same format.
This shared task focuses on determining causality associated with a quantified fact. An event is defined as the arising or emergence of a new object or context regarding a previous situation. So, the task will emphasise the detection of causality associated with the transformation of financial objects embedded in quantified facts.
*Spanish FinCausal subtask*
- *Data Description: *The dataset has been sourced from a corpus of Spanish financial annual reports from 2014 to 2018. Participants will be provided with a sample of text blocks extracted from financial news, labelled through inter-annotator agreement. - *Scope: *The *Spanish FinCausal subtask* aims to detect all types of causes and effects, not necessarily limited to quantified effects. 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. - *Length of Data fragments: *The *Spanish FinCausal subtask* involves complete paragraphs. - *Data format: *CSV files. Datasets for both the English and the Spanish subtasks will be presented in the same format.
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. This task is also a relation detection task. Best regards, FinCausal 2023 Team
*FinCausal 2023: Financial Document Causality Detection*
*Participation is still open for FinCausal 2023.*
Training Dataset for both English (updated & revised) and Spanish is released and ready. You can find it on Codalab in this link: https://codalab.lisn.upsaclay.fr/competitions/14596
Please register on CodaLab and get to the FInCausal.2023 Competition. Under Participate, you will find the Training Datasets together with a Starting Kit to guide you through the Task.
###### *Task Description and Important Links *#######
*FinCausal-2023 Shared Task: “Financial Document Causality Detection” *is organised within the *5th Financial Narrative Processing Workshop (FNP 2023)* taking place in the 2023 IEEE International Conference on Big Data (IEEE BigData 2023) http://bigdataieee.org/BigData2023/, Sorrento, Italy, 15-18 December 2023. It is a *one-day event*. Workshop URL: https://wp.lancs.ac.uk#####cfie/fincausal2023/ https://wp.lancs.ac.uk/cfie/fincausal2023/
###### *Additional Information *#######
*Shared Task Description:*
Financial analysis needs factual data and an explanation of the variability of these data. Data state facts but need more knowledge regarding how these facts materialised. Furthermore, understanding causality is crucial in studying decision-making processes.
The *Financial Document Causality Detection Task* (FinCausal) aims at identifying elements of cause and effect in causal sentences extracted from financial documents. Its goal is to evaluate which events or chain of events can cause a financial object to be modified or an event to occur, regarding a given context. In the financial landscape, identifying cause and effect from external documents and sources is crucial to explain why a transformation occurs.
Two subtasks are organised this year. *English FinCausal subtask *and* Spanish FinCausal subtask*. This is the first year where we introduce a subtask in Spanish.
*Objective*: For both tasks, participants are asked to identify, given a causal sentence, which elements of the sentence relate to the cause, and which relate to the effect. Participants can use any method they see fit (regex, corpus linguistics, entity relationship models, deep learning methods) to identify the causes and effects.
*English FinCausal subtask*
- *Data Description: *The dataset has been sourced from various 2019 financial news articles provided by Qwam, along with additional SEC data from the Edgar Database. Additionally, we have augmented the dataset from FinCausal 2022, adding 500 new segments. Participants will be provided with a sample of text blocks extracted from financial news and already labelled. - *Scope: *The* English FinCausal subtask* focuses on detecting causes and effects when the effects are quantified. The aim is to identify, in a causal sentence or text block, the causal elements and the consequential ones. Only one causal element and one effect are expected in each segment. - *Length of Data fragments: *The* English FinCausal subtask* segments are made up of up to three sentences. - *Data format: *CSV files. Datasets for both the English and the Spanish subtasks will be presented in the same format.
This shared task focuses on determining causality associated with a quantified fact. An event is defined as the arising or emergence of a new object or context regarding a previous situation. So, the task will emphasise the detection of causality associated with the transformation of financial objects embedded in quantified facts.
*Spanish FinCausal subtask*
- *Data Description: *The dataset has been sourced from a corpus of Spanish financial annual reports from 2014 to 2018. Participants will be provided with a sample of text blocks extracted from financial news, labelled through inter-annotator agreement. - *Scope: *The *Spanish FinCausal subtask* aims to detect all types of causes and effects, not necessarily limited to quantified effects. 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. - *Length of Data fragments: *The *Spanish FinCausal subtask* involves complete paragraphs. - *Data format: *CSV files. Datasets for both the English and the Spanish subtasks will be presented in the same format.
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. This task is also a relation detection task. Best regards, FinCausal 2023 Team
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FinCausal 2023