Financial Document Causality Detection Shared Task (FinCausal 2020)

TL;DR

The FinCausal 2020 Shared Task focused on developing systems for causality detection in financial documents, involving classification and relation extraction, with participation from 16 teams and multiple system submissions.

Contribution

This paper introduces the FinCausal dataset and benchmark tasks for causality detection in financial texts, fostering research in financial NLP and causality analysis.

Findings

Multiple systems achieved competitive performance on causality detection tasks.

The dataset facilitated benchmarking and comparison of causality extraction methods.

Participation from diverse teams advanced the state-of-the-art in financial causality analysis.

Abstract

We present the FinCausal 2020 Shared Task on Causality Detection in Financial Documents and the associated FinCausal dataset, and discuss the participating systems and results. Two sub-tasks are proposed: a binary classification task (Task 1) and a relation extraction task (Task 2). A total of 16 teams submitted runs across the two Tasks and 13 of them contributed with a system description paper. This workshop is associated to the Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation (FNP-FNS 2020), held at The 28th International Conference on Computational Linguistics (COLING'2020), Barcelona, Spain on September 12, 2020.