MONETECH at the NTCIR-17 FinArg-1 Task: Layer Freezing, Data Augmentation, and Data Filtering for Argument Unit Identification
Published in The 17th NTCIR Conference Evaluation of Information Access Technologies, 2023
Abstract
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.
Recommended citation:
Supawich Jiarakul, Hiroaki Yamada and Takenobu Tokunaga. MONETECH at the NTCIR-17 FinArg-1 Task: Layer Freezing, Data Augmentation, and Data Filtering for Argument Unit Identification. The 17th NTCIR Conference Evaluation of Information Access Technologies, 2023/12/12.