The JRIRD team demonstrates effective argument classification in budget argument mining, suggesting improvements for future applications.
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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Kadowaki et al. (2022) studied this question.
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