@inproceedings{gupta-etal-2026-resolvenow,
    title = "{RESOLVENOW} at {UZH} Shared Task 2026: Rule-Based Type Classification with {LLM}-Driven Multi-Label Tagging for {UN} Resolutions",
    author = "Gupta, Vedant  and
      Bhatia, Rahul  and
      Varshney, Vaibhav  and
      Naik, Manjunatha",
    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-1.12/",
    pages = "105--108",
    ISBN = "979-8-89176-399-9",
    abstract = "Subtask 1 of the UZH Shared Task 2026 asks for paragraph-level classification of UN resolutions as preambular or operative and multi-label tagging from a 141-code, 15-dimension taxonomy, scored by tag F1 and an open-weight LLM-as-Judge on reasoning quality. Two earlier pipelines we built failed in opposite ways. An embedding-retrieval system dropped relevant tags before the LLM saw them; a per-dimension prompting system was accurate but too slow to iterate. The submitted system fixes both. A deterministic French-English lexical classifier assigns paragraph types at type macro-F1 of 0.910 on the official silver standard with no LLM calls, and DeepSeek-R1-0528-Qwen3-8B predicts tags through a single merged prompt that exposes the full taxonomy."
}