What question did this study set out to answer?

To improve budget argument mining through the implementation of BERT models for classification and detection.

April 1, 2026Open Access

JRIRD at the NTCIR-16 QA Lab-PoliInfo-3 Budget Argument Mining

Key Points

To improve budget argument mining through the implementation of BERT models for classification and detection.
Utilized two independent BERT models for tasks: argument classification and related ID detection.
Participated in the NTCIR-16 QA Lab-PoliInfo-3 budget argument mining subtask.
Analyzed official results to evaluate system performance.
Achieved good performance in argument classification tasks.
Showed promising results for related ID detection as well.

Abstract

The JRIRD team participated in the budget argument mining subtask of the NTCIR-16 QA Lab-PoliInfo-3. This paper reports on our approach to solving this problem and discusses the official results. Our system consists of two BERT models that work independently toward two objectives: argument classification (AC) and related ID detection (RID). The results show that our system performs well, especially for argument classification.

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Cite This Study

Kadowaki et al. (Tue,) studied this question.

synapsesocial.com/papers/69cd7b695652765b073a96a7 https://doi.org/https://doi.org/10.20736/0002002286

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