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)
- Venue:
- FNP
- SIG:
- Publisher:
- COLING
- Note:
- Pages:
- 40–44
- Language:
- URL:
- https://aclanthology.org/2020.fnp-1.5
- DOI:
- 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)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.fnp-1.5.pdf
- Code
- guillaume-be/financial-causality-extraction
- Data
- SQuAD