GBe at FinCausal 2020, Task 2: Span-based Causality Extraction for Financial Documents

Guillaume Becquin


Abstract
This document describes a system for causality extraction from financial documents submitted as part of the FinCausal 2020 Workshop. The main contribution of this paper is a description of the robust post-processing used to detect the number of cause and effect clauses in a document and extract them. The proposed system achieved a weighted-average F1 score of more than 95% for the official blind test set during the post-evaluation phase and exact clauses match for 83% of the documents.
Anthology ID:
2020.fnp-1.5
Volume:
Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Editors:
Dr Mahmoud El-Haj, Dr Vasiliki Athanasakou, Dr Sira Ferradans, Dr Catherine Salzedo, Dr Ans Elhag, Dr Houda Bouamor, Dr Marina Litvak, Dr Paul Rayson, Dr George Giannakopoulos, Nikiforos Pittaras
Venue:
FNP
SIG:
Publisher:
COLING
Note:
Pages:
40–44
Language:
URL:
https://aclanthology.org/2020.fnp-1.5
DOI:
Bibkey:
Cite (ACL):
Guillaume Becquin. 2020. GBe at FinCausal 2020, Task 2: Span-based Causality Extraction for Financial Documents. In Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation, pages 40–44, Barcelona, Spain (Online). COLING.
Cite (Informal):
GBe at FinCausal 2020, Task 2: Span-based Causality Extraction for Financial Documents (Becquin, FNP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.fnp-1.5.pdf
Code
 guillaume-be/financial-causality-extraction
Data
SQuAD