TMUNLP at the NTCIR-17 FinArg-1 Task

The TMUNLP team participated in the FinArg-1 Task of NTCIR- 17, focusing on Argument Unit Identification and Argument Relation Identification in the finance domain using social media and earnings call datasets. Notably, the team ranked 1st and 3rd in these subtasks, respectively. This paper presents the team's methodologies, results, and conclusions. For Earning Conference Call (ECC) Argument Unit Identification, an ensemble strategy combining diverse pre-trained models achieved a Macro F1 score of 0.766231, with significant contributions from models like ELECTRA, RoBERTa, BERT-base-uncased, and FinBERT. In ECC Argument Relation Identification, a combination of pretrained models and sampling strategies, along with voting mechanisms, improved natural language inference tasks. Future research opportunities include optimizing integration methods for semantic inference efficiency. Finally, in Social Media (SM) Argument Relation Identification, ChatGPT's keyword features positively impacted model performance. Challenges of translation and data imbalance were addressed through category-weighted sampling methods and soft voting, showcasing adaptable strategies. This study highlights the efficacy of ensemble strategies and diverse models in NLP tasks and emphasizes potential advancements in the field.