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Span-based Causality Extraction for Financial Documents

This repository contains the supporting code for the FinCausal 2020 submission titled Span-based Causality Extraction for Financial Documents. The model extracts cause and effect spans from financial documents, for example:

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

(sample from the task description: Data Processing and Metrics for FinCausal Shared Task, 2020, Mariko et al.)

The system ranked 2nd on the official evaluation board, and reached the following performance in post-evaluation:

Post-evaluation performance

Metric score
weighted-averaged F1 95.01%
Exact matches 83.34%
weighted-averaged Precision 95.01%
weighted-averaged Recall 95.01%

Official evaluation performance

Metric score
weighted-averaged F1 94.66%
Exact matches 73.66%
weighted-averaged Precision 94.66%
weighted-averaged Recall 94.66%

The system is based on a RoBERTa span-extraction model (similar to Question Answering architecture), a full description of the system is available in the related system description. If you find this system useful, please cite us:

@inproceedings{
 Becquin-fincausal-2020, 
 title ={{GBe at FinCausal 2020, Task 2: Span-based Causality Extraction for Financial Documents}}, 
 author = {Becquin, Guillaume}, 
 booktitle ={{The 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation (FNP-FNS 2020}}, 
 year = {2020}, 
 address = {Barcelona, Spain} 
}

Instructions

  1. Install requirements provided in reuiquirements.py (it is advised to use a virtual environment)
  2. Generate the train / development data split running running the ./utils/split_dataset.py

Training:

  1. run main.py --train

Evaluation:

  1. run main.py --eval

Generate predictions:

  1. run main.py --test