NTUNLPL at FinCausal 2020, Task 2:Improving Causality Detection Using Viterbi Decoder

Pei-Wei Kao, Chung-Chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen


Abstract
In order to provide an explanation of machine learning models, causality detection attracts lots of attention in the artificial intelligence research community. In this paper, we explore the cause-effect detection in financial news and propose an approach, which combines the BIO scheme with the Viterbi decoder for addressing this challenge. Our approach is ranked the first in the official run of cause-effect detection (Task 2) of the FinCausal-2020 shared task. We not only report the implementation details and ablation analysis in this paper, but also publish our code for academic usage.
Anthology ID:
2020.fnp-1.11
Volume:
Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venues:
COLING | FNP
SIG:
Publisher:
COLING
Note:
Pages:
69–73
Language:
URL:
https://aclanthology.org/2020.fnp-1.11
DOI:
Bibkey:
Cite (ACL):
Pei-Wei Kao, Chung-Chi Chen, Hen-Hsen Huang, and Hsin-Hsi Chen. 2020. NTUNLPL at FinCausal 2020, Task 2:Improving Causality Detection Using Viterbi Decoder. In Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation, pages 69–73, Barcelona, Spain (Online). COLING.
Cite (Informal):
NTUNLPL at FinCausal 2020, Task 2:Improving Causality Detection Using Viterbi Decoder (Kao et al., FNP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.fnp-1.11.pdf
Code
 pxpxkao/fincausal-2020