Overview of the UZH Shared Task 2026 on Reconstructing the Reasoning in United Nations Resolutions
Anastassia Shaitarova, Yingqiang Gao, Fatma-Zohra Rezkellah, Reto Gubelmann, Patrick Montjouridès
Abstract
This paper presents the UZH Shared Task at the 13th Workshop on Argument Mining and Reasoning, co-located with ACL 2026, which focuses on reconstructing argumentative structure in highly formal legal-political texts, namely United Nations resolutions and recommendations. The shared task addresses the challenge of recovering paragraph-level reasoning patterns from the fairly formulaic structure of international decision-making records. It comprises two subtasks: (1) paragraph classification, where systems identify paragraph type (preambular or operative) and assign one or more thematic tags, and (2) argumentative relation prediction, where systems infer links between paragraphs and label them with relation types.- Anthology ID:
- 2026.argmining-1.10
- Volume:
- Proceedings of the 13th Workshop on Argument Mining and Reasoning
- Month:
- July
- Year:
- 2026
- Address:
- San Diego, California, USA
- Editors:
- Mohamed Elaraby, Annette Hautli-Janisz, Julia Romberg, Elena Musi, Federico Ruggeri, John Lawrence
- Venues:
- ArgMining | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 87–98
- Language:
- URL:
- https://preview.aclanthology.org/ingest-acl-workshops/2026.argmining-1.10/
- DOI:
- Cite (ACL):
- Anastassia Shaitarova, Yingqiang Gao, Fatma-Zohra Rezkellah, Reto Gubelmann, and Patrick Montjouridès. 2026. Overview of the UZH Shared Task 2026 on Reconstructing the Reasoning in United Nations Resolutions. In Proceedings of the 13th Workshop on Argument Mining and Reasoning, pages 87–98, San Diego, California, USA. Association for Computational Linguistics.
- Cite (Informal):
- Overview of the UZH Shared Task 2026 on Reconstructing the Reasoning in United Nations Resolutions (Shaitarova et al., ArgMining 2026)
- PDF:
- https://preview.aclanthology.org/ingest-acl-workshops/2026.argmining-1.10.pdf