SCUNLP-2 at the NTCIR-17 FinArg-1 Task: Enhancing Argumentative Relationship Recognition in the Classification Model with Language Generation Model Prompts

While argument mining has significantly advanced across various domains, its application to financial discussions remains relatively unexplored. Our motivation for this research is rooted in the understanding that sentiment analysis alone may be inadequate when evaluating financial discussions, as the financial world is influenced by many factors intricately intertwined with the sentiments and opinions expressed by investors, analysts, and policymakers. To enhance the analysis of financial arguments, we incorporate GPT into the field of financial argument mining and design custom prompts. This unique integration allows us to generate labels and summaries for the arguments extracted from social media discussions. Our research results indicate that adding the generated labels in the regular mode achieved the highest validation set Marco-F1 score (66.39%). These findings contribute to a deeper understanding of argument mining in financial and social media discussions.