@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://aclanthology.org/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."
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%0 Conference Proceedings
%T HybridArguer at UZH Shared Task 2026: Argument Structure Modeling in Bilingual UN Resolutions with Retrieval-Augmented and Iterative LLM Reasoning
%A Bhargava, Siddharth
%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 bhargava-2026-hybridarguer
%X 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.
%U https://aclanthology.org/2026.argmining-1.17/
%P 131-139
Markdown (Informal)
[HybridArguer at UZH Shared Task 2026: Argument Structure Modeling in Bilingual UN Resolutions with Retrieval-Augmented and Iterative LLM Reasoning](https://aclanthology.org/2026.argmining-1.17/) (Bhargava, ArgMining 2026)
ACL