TUA1 at NTCIR-17 FinArg-1 Task

This paper reports on the results produced by the TUA1 team in the Earnings Conference Call (ECC), Task 1 of Finarg-1 of NTCIR-17. The ECC is divided into two subtasks. One is Argument Unit Identification (AUI) and the other is Argument Relation Identification (ARI). There are two proposed methods. The first is to tune a pre-trained model based on the transformer architecture using prompts. This method was applied to both Argument Unit Identification and Argument Relation Identification. The second approach employs Cost-Sensitive Learning on pre-trained models, which were previously tuned. This was exclusively used for Argument Relation Identification.In the provided training and validation data for Argument Relation Identification, the correct labels were markedly unbalanced, with some specific labels being notably scarce. Cost-Sensitive Learning proves effective for such unbalanced datasets, often yielding higher results than pure pre-trained models alone. In our experiments involving prompt tuning, we leveraged the Weighted Random Sampler technique to further enhance accuracy on the unbalanced data.Experiments using the aforementioned methods revealed that we achieved the best results for Argument Relation Identification, and secured third place for Argument Unit Identification.