JDD @ FinCausal 2020, Task 2: Financial Document Causality Detection

Toshiya Imoto, Tomoki Ito

Abstract
This paper describes the approach we built for the Financial Document Causality Detection Shared Task (FinCausal-2020) Task 2: Cause and Effect Detection. Our approach is based on a multi-class classifier using BiLSTM with Graph Convolutional Neural Network (GCN) trained by minimizing the binary cross entropy loss. In our approach, we have not used any extra data source apart from combining the trial and practice dataset. We achieve weighted F1 score to 75.61 percent and are ranked at 7-th place.
Anthology ID:
2020.fnp-1.7
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:
50–54
Language:
URL:
https://aclanthology.org/2020.fnp-1.7
DOI:
Bibkey:
Cite (ACL):
Toshiya Imoto and Tomoki Ito. 2020. JDD @ FinCausal 2020, Task 2: Financial Document Causality Detection. In Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation, pages 50–54, Barcelona, Spain (Online). COLING.
Cite (Informal):
JDD @ FinCausal 2020, Task 2: Financial Document Causality Detection (Imoto & Ito, FNP 2020)
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