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This work introduces a novel approach that leverages scientific entity mentions to identify and structure claims in scholarly literature to address the annotation bottleneck and employs a majority-voting ensemble of large language models to generate a silver-standard dataset comprising 1,487 claims.
A BERT-BiLSTM-CRF architecture for argument discourse detection and classification, as the system for Daleel 2026, the inaugural Arabic argument mining shared task, is presented.
This work introduces $\texttt{CLASP-Ar}$, which reformulates the Arabic-language stance detection task as cloze-style masked language modeling, and reformulates the task as cloze-style masked language modeling.
While TTLab's system localizes error spans effectively, classification of rare error types remains challenging, highlighting the need for data augmentation for tail categories.
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