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TTLab at Daleel 2026: STAR-Ar, Sequence Tagging for Argument Recognition in Arabic — arXiv:2609.39385

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.39385
License (as archived): CC0-1.0
Archived: 2026-10-07T12:45:21.169Z
SHA-256 of the archived text: 8497de1a87f1a889ee2447fb9fcd608397f55626fd94c76000133e682c083a8b
Stable URL of this page: https://metraipolis.com/documento/dc_d08ffa99542e

How to cite

TTLab at Daleel 2026: STAR-Ar, Sequence Tagging for Argument Recognition in Arabic — arXiv:2609.39385 — arXiv (en), https://arxiv.org/abs/2609.39385 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_d08ffa99542e (Metraipolis archive dc_d08ffa99542e, sha256 8497de1a87f1a889ee2447fb9fcd608397f55626fd94c76000133e682c083a8b)

BibTeX

@misc{dcd08ffa99542e,
  title = {TTLab at Daleel 2026: STAR-Ar, Sequence Tagging for Argument Recognition in Arabic — arXiv:2609.39385},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.39385},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_d08ffa99542e; sha256 8497de1a87f1a889ee2447fb9fcd608397f55626fd94c76000133e682c083a8b},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.39385. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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 ${\href{ this https URL }{\faGithub~TTLab at Daleel 2026}}$

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-10-01 in cs.AI).
Categorie arXiv: Computation and Language (cs.CL) ; Artificial Intelligence (cs.AI); Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2609.39385 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T12:14:14.076Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-10-01?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).