@inproceedings{xu-etal-2022-ilab,
title = "i{L}ab at {F}in{C}ausal 2022: Enhancing Causality Detection with an External Cause-Effect Knowledge Graph",
author = "Xu, Ziwei and
Nararatwong, Rungsiman and
Kertkeidkachorn, Natthawut and
Ichise, Ryutaro",
editor = "El-Haj, Mahmoud and
Rayson, Paul and
Zmandar, Nadhem",
booktitle = "Proceedings of the 4th Financial Narrative Processing Workshop @LREC2022",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.fnp-1.21/",
pages = "124--127",
abstract = "The application of span detection grows fast along with the increasing need of understanding the causes and effects of events, especially in the finance domain. However, once the syntactic clues are absent in the text, the model tends to reverse the cause and effect spans. To solve this problem, we introduce graph construction techniques to inject cause-effect graph knowledge for graph embedding. The graph features combining with BERT embedding, then are used to predict the cause effect spans. The results show our proposed graph builder method outperforms the other methods w/wo external knowledge."
}
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<abstract>The application of span detection grows fast along with the increasing need of understanding the causes and effects of events, especially in the finance domain. However, once the syntactic clues are absent in the text, the model tends to reverse the cause and effect spans. To solve this problem, we introduce graph construction techniques to inject cause-effect graph knowledge for graph embedding. The graph features combining with BERT embedding, then are used to predict the cause effect spans. The results show our proposed graph builder method outperforms the other methods w/wo external knowledge.</abstract>
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%0 Conference Proceedings
%T iLab at FinCausal 2022: Enhancing Causality Detection with an External Cause-Effect Knowledge Graph
%A Xu, Ziwei
%A Nararatwong, Rungsiman
%A Kertkeidkachorn, Natthawut
%A Ichise, Ryutaro
%Y El-Haj, Mahmoud
%Y Rayson, Paul
%Y Zmandar, Nadhem
%S Proceedings of the 4th Financial Narrative Processing Workshop @LREC2022
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F xu-etal-2022-ilab
%X The application of span detection grows fast along with the increasing need of understanding the causes and effects of events, especially in the finance domain. However, once the syntactic clues are absent in the text, the model tends to reverse the cause and effect spans. To solve this problem, we introduce graph construction techniques to inject cause-effect graph knowledge for graph embedding. The graph features combining with BERT embedding, then are used to predict the cause effect spans. The results show our proposed graph builder method outperforms the other methods w/wo external knowledge.
%U https://aclanthology.org/2022.fnp-1.21/
%P 124-127
Markdown (Informal)
[iLab at FinCausal 2022: Enhancing Causality Detection with an External Cause-Effect Knowledge Graph](https://aclanthology.org/2022.fnp-1.21/) (Xu et al., FNP 2022)
ACL