FinCausal-2020 Shared Task: “Financial Document Causality Detection”
To be held at The 3rd Financial Narrative Processing Workshop (FNP 2021), Lancaster, United Kingdom [online] on 15 September 2021.
Important Dates:
1st Call for participation: 1 May 20212nd Call for participation: 15 May 2021Training set release: 1st of June 2021Blind test set release: 1st of July 2021.- Systems submission 1st of September 2021 (extended)
- Release of results: 1st of September 2021
Awards and Prizes:
The winning team for FinCausal 2021 shared task will receive an achievement certificate and a money prize worth US$650. The team will also be given the chance to present their work at the workshop.
Introduction
Financial analysis needs factual data, but also explanation on the variability of these data. Data state facts, but provide little to no knowledge regarding how these facts materialised. The Financial Document Causality Detection Task aims to develop an ability to explain, from external sources, the reasons why a transformation occurs in the financial landscape, as a preamble to generating accurate and meaningful financial narrative summaries. Its goal is to evaluate which events or which chain of events can cause a financial object to be modified or an event to occur, regarding a given external context. This context is available in the financial news, but due to the high volatility of such information, mapping an external cause to a given consequence is not trivial.
The task dataset has been extracted from different 2019 financial news kindly provided by Qwam, and additional SEC data from the Edgar Database, and has been normalised for the research task.
Participants will be asked to detect, in causal sentences, which elements of the sentence relate to the cause and which relate to the effect.
This paper details the data processing and the labelling scheme used to create the training data for this task.
Task
As part of the Financial Narrative workshop, we propose the FinCausal Task, focusing on detecting if an object, an event or a chain of events is considered a cause for a prior event. 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 in regard to a previous situation. So the task will emphasise the detection of causality associated with transformation of financial objects embedded in quantified facts.
Participants will be provided with a sample of text blocks extracted from financial news, labelled through inter annotator agreement.
Cause and Effect Detection
This task is a relation detection task. The aim is to identify, in a causal sentence or text block, the causal elements and the consequential ones.
| Text | Cause | Effect |
| Boussard Gavaudan Investment Management LLP bought a new position in shares of GENFIT S A/ADR in the second quarter worth about $199,000. Morgan Stanley increased its stake in shares of GENFIT S A/ADR by 24.4% in the second quarter.Morgan Stanley now owns 10,700 shares of the company’s stock worth $211,000 after purchasing an additional 2,100 shares during the period | Morgan Stanley increased its stake in shares of GENFIT S A/ADR by 24.4% in the second quarter | Morgan Stanley now owns 10,700 shares of the company’s stock worth $211,000 after purchasing an additional 2,100 shares during the period. |
| Zhao found himself 60 million yuan indebted after losing 9,000 BTC in a single day (February 10, 2014) | losing 9,000 BTC in a single day (February 10, 2014) | Zhao found himself 60 million yuan indebted |
Table 1: Cause and Effect Detection Sample
Participants are free to use any method they see fit (regex, corpus linguistics, entity relationship models, deep learning methods) to identify the causes and effects.
Previous Work
This shared task has been proposed in FNP 2020 FinCausal workshop, presented at COLING 2020. Proceedings of the workshop are available.
Shared task Paper Submission Instructions:
Submission URL: TBA
FinCausal Shared Task Organisers
- Dominique Mariko – Yseop Lab
- Hanna Abi Akl – Yseop Lab
- Hugues de Mazancourt – Yseop Lab
- Estelle Labidurie – Yseop Lab
Shared Task Contact
The participants to this task will access the data after registering, and thereby pledge to contribute to the workshop by submitting an experiment paper.
Participant can register for this shared task by filling this form, and get access to the datasets.
For any question please contact the organisers at fin.causal.task@gmail.com