This paper presents our participation in FinArg-2, which succceeds the FinArg-1 task. While FinArg-1 focused on sentiment analysis and argument classification, FinArg-2 extends this to temporal. We experiment with a method of classifying text into two types: "Premise" and "Claim." Based on these premises and claim, we have developed a method suitable for accurately classifying the temporal relationships between sentences. In order to classify sentences, we trained a classification model on labeled data, and compared traditional machine learning approaches with models that use large scale language models. Among the models tested, DeBERTa and Llama achieved the highest classification accuracy, demonstrating the model that used a large-scale language model showed auperior results.
Kawamoto et al. (2025) studied this question.