The increasing availability of financial texts from earnings conference calls (ECCs) and social media has created a need for advanced natural language processing (NLP) techniques to extract meaningful insights. This study develops a classification framework that integrates fine-tuning and prompt-based learning to improve financial argument classification. We apply this framework to two tasks from the NTCIR-18 FinArg-2 competition: detecting temporal references in ECCs and assessing the validity period of claims in social media. Encoder-based models are fine-tuned for structured classification, while decoder-based models leverage both fine-tuning and prompt-based learning. Data augmentation techniques enhance model generalization, and performance is evaluated using Micro-F1 and Macro-F1 scores. The primary contribution of this research is demonstrating how fine-tuning and prompt-based learning can complement each other in financial NLP. By optimizing classification strategies, this study provides insights for improving argument analysis in financial applications, benefiting researchers, practitioners, and FinTech developers.
Chen et al. (2025) studied this question.