@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://aclanthology.org/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."
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%0 Conference Proceedings
%T RESOLVENOW at UZH Shared Task 2026: Rule-Based Type Classification with LLM-Driven Multi-Label Tagging for UN Resolutions
%A Gupta, Vedant
%A Bhatia, Rahul
%A Varshney, Vaibhav
%A Naik, Manjunatha
%Y Elaraby, Mohamed
%Y Hautli-Janisz, Annette
%Y Romberg, Julia
%Y Musi, Elena
%Y Ruggeri, Federico
%Y Lawrence, John
%S Proceedings of the 13th Workshop on Argument Mining and Reasoning
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-399-9
%F gupta-etal-2026-resolvenow
%X 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.
%U https://aclanthology.org/2026.argmining-1.12/
%P 105-108
Markdown (Informal)
[RESOLVENOW at UZH Shared Task 2026: Rule-Based Type Classification with LLM-Driven Multi-Label Tagging for UN Resolutions](https://aclanthology.org/2026.argmining-1.12/) (Gupta et al., ArgMining 2026)
ACL