@inproceedings{kao-etal-2020-ntunlpl,
title = "{NTUNLPL} at {F}in{C}ausal 2020, Task 2:Improving Causality Detection Using {V}iterbi Decoder",
author = "Kao, Pei-Wei and
Chen, Chung-Chi and
Huang, Hen-Hsen and
Chen, Hsin-Hsi",
editor = "El-Haj, Dr Mahmoud and
Athanasakou, Dr Vasiliki and
Ferradans, Dr Sira and
Salzedo, Dr Catherine and
Elhag, Dr Ans and
Bouamor, Dr Houda and
Litvak, Dr Marina and
Rayson, Dr Paul and
Giannakopoulos, Dr George and
Pittaras, Nikiforos",
booktitle = "Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "COLING",
url = "https://aclanthology.org/2020.fnp-1.11",
pages = "69--73",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T NTUNLPL at FinCausal 2020, Task 2:Improving Causality Detection Using Viterbi Decoder
%A Kao, Pei-Wei
%A Chen, Chung-Chi
%A Huang, Hen-Hsen
%A Chen, Hsin-Hsi
%Y El-Haj, Dr Mahmoud
%Y Athanasakou, Dr Vasiliki
%Y Ferradans, Dr Sira
%Y Salzedo, Dr Catherine
%Y Elhag, Dr Ans
%Y Bouamor, Dr Houda
%Y Litvak, Dr Marina
%Y Rayson, Dr Paul
%Y Giannakopoulos, Dr George
%Y Pittaras, Nikiforos
%S Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation
%D 2020
%8 December
%I COLING
%C Barcelona, Spain (Online)
%F kao-etal-2020-ntunlpl
%X 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.
%U https://aclanthology.org/2020.fnp-1.11
%P 69-73
Markdown (Informal)
[NTUNLPL at FinCausal 2020, Task 2:Improving Causality Detection Using Viterbi Decoder](https://aclanthology.org/2020.fnp-1.11) (Kao et al., FNP 2020)
ACL