Submission guidelines: http://wp.lancs.ac.uk/cfie/fnp2020/guidelines/

Participation Form: Register a new team

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 evaluate whether a sentence is causal or not (Task 1), then to detect, in causal sentences, which elements of the sentence relate to the cause and which relate to the effect (Task 2).

This paper details the data processing and the labelling scheme, the expected results and the metrics used for evaluation. It will be updated on release of the training data.

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 to a quantified fact. An event is defined as the arising or emergence of a new object or context in regard of a previous situation. So the task will emphasise the detection of causality associated to transformation of financial objects embedded in quantified facts.

Participants will be provided with a sample of text blocks extracted from financial news and SEC data, labelled through inter annotator agreement.

 The Shared Task contains two sub-tasks:

Task 1: Sentence Classification

This task is a binary classification task. The goal of this subtask is to filter sentences which display causal meanings (1) from the sentences that are noise in regard of causality (0)

Task 2: 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.

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.

Shared task Paper Submission Instructions:

Submission URL: https://www.softconf.com/coling2020/FNP-FNS/

Detailed submission guidelines can be found here: http://wp.lancs.ac.uk/cfie/fnp2020/guidelines/

Shared Task Organisers

• Dominique Mariko – Yseop Lab
• Hanna Abi Akl – Yseop Lab
• Hugues de Mazancourt – Yseop Lab
• Estelle Labidurie – Yseop Lab
• Stephane Durfort – 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 to 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