@inproceedings{bhargava-2026-hybridarguer,
    title = "{H}ybrid{A}rguer at {UZH} Shared Task 2026: Argument Structure Modeling in Bilingual {UN} Resolutions with Retrieval-Augmented and Iterative {LLM} Reasoning",
    author = "Bhargava, Siddharth",
    editor = "Elaraby, Mohamed  and
      Hautli-Janisz, Annette  and
      Romberg, Julia  and
      Musi, Elena  and
      Ruggeri, Federico  and
      Lawrence, John",
    booktitle = "Proceedings of the 13th Workshop on Argument Mining and Reasoning",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.argmining-1.17/",
    pages = "131--139",
    ISBN = "979-8-89176-399-9",
    abstract = "Extracting argument structures from legal-political discourse reveals how policies and actions are proposed, debated, and formalized, but remains challenging due to the complexity of long-form, structured text. This work proposes a modular, retrieval-augmented system for traceable and structured argument mining in long, bilingual United Nations resolutions. This paper describes our system submission to the UZH Shared Task 2026, focusing on practical design choices for argument structure modeling under task and model constraints. Our system employs a parameter-efficient (at most 8B) open-source model, Qwen3:8B in thinking mode, to perform paragraph classification, multi-label tag assignment, and multi-label relation prediction through a modular, retrieval-augmented pipeline."
}