TTLab at Daleel 2026: STAR-Ar, Sequence Tagging for Argument Recognition in Arabic

Abstract:Argument Mining (AM) is a critical NLP task that remains significantly under-resourced in Arabic. This paper presents $\testtt{STAR-Ar}$, a BERT-BiLSTM-CRF architecture for argument discourse detection and classification, as our system for Daleel 2026, the inaugural Arabic argument mining shared task. The task requires the identification and classification of argumentative discourse units (ADUs) in debate and editorial this http URL jointly model these two objectives as a token-level sequence labeling task using a BERT-BiLSTM-CRF architecture that combines contextual transformer embeddings with structural transition constraints to support accurate span detection. $\testtt{STAR-Ar}$ achieves an F1-score of 72.69 on validation and 73.7 on test data. Our domain-specific analysis shows that models trained exclusively on editorials underperform those trained on debates, a disparity we primarily attribute to the smaller size of the editorial dataset. The code for $\testtt{STAR-Ar}$ is available at ${\href{this https URL}{\faGithub~TTLab at Daleel 2026}}$

Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2609.39385 [cs.CL]

(or arXiv:2609.39385v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.39385 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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