What question did this study set out to answer?

The aim is to improve the classification of argumentative relations in online financial discussions using optimized Transformer models.

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April 1, 2026Open Access

Quack at the NTCIR-17 FinArg-1 Task : Boosting and MLM Enhanced Financial Knowledge Sequence Classification

Key Points

The aim is to improve the classification of argumentative relations in online financial discussions using optimized Transformer models.
Fine-tuned BERT Transformer model for classification tasks.
Incorporated Masked Language Model technique to enhance domain knowledge.
Adjusted loss function weights to optimize model performance.
Implemented methods effectively enhanced the Transformer model’s classification performance.
Demonstrated a clear distinction between proponents and adversaries in financial discussions.

Abstract

In the exploration of the task "Identifying Attack and Support Argumentative Relations in Social Media Discussion Threads" (SMDT) 1, we aim to discern differences between proponents and adversaries in financial discussions on the internet. For classification tasks such as these, fine-tuning Transformer models like BERT 2 is an intuitive approach. In this study, we build upon this foundation by incorporating the Masked Language Model technique to enrich the model’s domain knowledge within the financial field. Furthermore, we optimize the model’s performance by adjusting the weights in the loss function. Experimental results confirm that both methods effectively enhance the model’s performance. This research introduces three simple yet effective methods to improve the Transformer model’s ability for SMDT. The code and model for this study are available at https://github.com/leonardo-lin/NTCIR.

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