Medha Jeenoor

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2025

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PresiUniv at FinCausal 2025 Shared Task: Applying Fine-tuned Language Models to Explain Financial Cause and Effect with Zero-shot Learning
Medha Jeenoor | Madiha Aziz | Saipriya Dipika Vaidyanathan | Avijit Samantraya | Sandeep Mathias
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)

Transformer-based multilingual question-answering models are used to detect causality in financial text data. This study employs BERT (CITATION) for English text and XLM-RoBERTa (CITATION) for Spanish data, which were fine-tuned on the SQuAD datasets (CITATION) (CITATION). These pre-trained models are used to extract answers to the targeted questions. We design a system using these pre-trained models to answer questions, based on the given context. The results validate the effectiveness of the systems in understanding nuanced financial language and offers a tool for multi-lingual text analysis. Our system is able to achieve SAS scores of 0.75 in Spanish and 0.82 in English.
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