MONETECH at the NTCIR-17 FinArg-1 Task: Layer Freezing, Data Augmentation, and Data Filtering for Argument Unit Identification

This paper reports MONETECH's participation in FinArg-1's Argument Unit Identification in Earnings Conference Call subtask. Our experiments are based on the BERT and FinBERT models with additional experimentation on Large Language Model-based data augmentation, data filtering, and the model's layer freezing. Our best-performing submission, which is based on data filtering and the model's layer freezing, scores 75.54\% in micro F1 evaluation. Results from additional runs also show that the model's layer freezing and data filtering could further improve model performance beyond our best submission.