@inproceedings{k-s-etal-2025-clrg,
title = "{CLRG}@{F}in{C}ausal2025: Cause-Effect Extraction in Finance Domain",
author = "K S, Vibhavkrishnan and
RK Rao, Pattabhi and
Lalitha Devi, Sobha",
editor = "Chen, Chung-Chi and
Moreno-Sandoval, Antonio and
Huang, Jimin and
Xie, Qianqian and
Ananiadou, Sophia and
Chen, Hsin-Hsi",
booktitle = "Proceedings of the Joint Workshop of the 9th Financial Technology and Natural Language Processing (FinNLP), the 6th Financial Narrative Processing (FNP), and the 1st Workshop on Large Language Models for Finance and Legal (LLMFinLegal)",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.finnlp-1.24/",
pages = "236--241",
abstract = "This paper presents our work on Cause-Effect information extraction specifically in the financial domain. Cause and effect information is very much needed for expert decision making. Particularly, in the financial domain, the fund managers, financial analysts, etc. need to have the information on cause-effects for their works. Natural Language Processing (NLP) techniques help in the automatic extraction of cause and effect from a given text. In this work, we build various cause-effect text span detection models using pre-trained transformer-based language models and fine tune these models using the data provided by FinCausal 2025 task organizers. We have only used FinCausal 2025 data sets to train our models. No other external data is used. Our ensemble of sequence tagging models based on the Fine-tuned RoBERTa-Large language model achieves SAS score of 0.9604 and Exact match score of 0.7214 for English. Similarly for Spanish we obtain SAS score of 0.9607 and Exact match score of 0.7166. This is our first time participation in the FinCausal 2025 Task."
}
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<abstract>This paper presents our work on Cause-Effect information extraction specifically in the financial domain. Cause and effect information is very much needed for expert decision making. Particularly, in the financial domain, the fund managers, financial analysts, etc. need to have the information on cause-effects for their works. Natural Language Processing (NLP) techniques help in the automatic extraction of cause and effect from a given text. In this work, we build various cause-effect text span detection models using pre-trained transformer-based language models and fine tune these models using the data provided by FinCausal 2025 task organizers. We have only used FinCausal 2025 data sets to train our models. No other external data is used. Our ensemble of sequence tagging models based on the Fine-tuned RoBERTa-Large language model achieves SAS score of 0.9604 and Exact match score of 0.7214 for English. Similarly for Spanish we obtain SAS score of 0.9607 and Exact match score of 0.7166. This is our first time participation in the FinCausal 2025 Task.</abstract>
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%0 Conference Proceedings
%T CLRG@FinCausal2025: Cause-Effect Extraction in Finance Domain
%A K S, Vibhavkrishnan
%A RK Rao, Pattabhi
%A Lalitha Devi, Sobha
%Y Chen, Chung-Chi
%Y Moreno-Sandoval, Antonio
%Y Huang, Jimin
%Y Xie, Qianqian
%Y Ananiadou, Sophia
%Y Chen, Hsin-Hsi
%S Proceedings of the Joint Workshop of the 9th Financial Technology and Natural Language Processing (FinNLP), the 6th Financial Narrative Processing (FNP), and the 1st Workshop on Large Language Models for Finance and Legal (LLMFinLegal)
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F k-s-etal-2025-clrg
%X This paper presents our work on Cause-Effect information extraction specifically in the financial domain. Cause and effect information is very much needed for expert decision making. Particularly, in the financial domain, the fund managers, financial analysts, etc. need to have the information on cause-effects for their works. Natural Language Processing (NLP) techniques help in the automatic extraction of cause and effect from a given text. In this work, we build various cause-effect text span detection models using pre-trained transformer-based language models and fine tune these models using the data provided by FinCausal 2025 task organizers. We have only used FinCausal 2025 data sets to train our models. No other external data is used. Our ensemble of sequence tagging models based on the Fine-tuned RoBERTa-Large language model achieves SAS score of 0.9604 and Exact match score of 0.7214 for English. Similarly for Spanish we obtain SAS score of 0.9607 and Exact match score of 0.7166. This is our first time participation in the FinCausal 2025 Task.
%U https://aclanthology.org/2025.finnlp-1.24/
%P 236-241
Markdown (Informal)
[CLRG@FinCausal2025: Cause-Effect Extraction in Finance Domain](https://aclanthology.org/2025.finnlp-1.24/) (K S et al., FinNLP 2025)
ACL
- Vibhavkrishnan K S, Pattabhi RK Rao, and Sobha Lalitha Devi. 2025. CLRG@FinCausal2025: Cause-Effect Extraction in Finance Domain. In Proceedings of the Joint Workshop of the 9th Financial Technology and Natural Language Processing (FinNLP), the 6th Financial Narrative Processing (FNP), and the 1st Workshop on Large Language Models for Finance and Legal (LLMFinLegal), pages 236–241, Abu Dhabi, UAE. Association for Computational Linguistics.