@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://preview.aclanthology.org/jlcl-multiple-ingestion/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."
}