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license: cc-by-nc-4.0 |
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language: |
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- en |
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- fr |
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size_categories: |
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- 1K<n<10K |
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--- |
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# Reconstructing the Reasoning in United Nations Resolutions |
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This repository contains the dataset for the **UZH Shared Task @ ArgMining Workshop 2026**, co-located with **ACL 2026**. |
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The shared task focuses on recovering **paragraph-level argumentative structure** in highly formal, legal-political documents, specifically **United Nations resolutions and recommendations**. |
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The dataset supports two subtasks: |
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1. Argumentative paragraph classification |
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2. Argumentative relation prediction between paragraphs |
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--- |
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## Claimer |
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**The content of this publication has not been approved by the United Nations and does not reflect the views of the United Nations or its officials or Member States. UN-RES should only be used for research purposes.** |
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## Contact |
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Please contact the shared task organizers at University of Zurich for questions. |
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## Task Overview |
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United Nations resolutions encode collective reasoning at scale through carefully structured preambles and operative clauses. |
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This shared task evaluates how well systems can reconstruct this implicit reasoning structure. |
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Participants are expected to build systems that: |
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- Identify whether a paragraph is **preambular** or **operative** |
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- Assign one or more **argumentative tags** |
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- Predict **argumentative relations** between paragraphs |
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Only **open-weight language models with ≤ 8B parameters** are permitted. |
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--- |
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## Subtasks |
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### Subtask 1: Argumentative Paragraph Classification |
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For each paragraph, systems must predict: |
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- **Paragraph type**: `preambular` or `operative` |
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- **Argumentative tags**: multi-label classification over a predefined tag set |
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### Subtask 2: Argumentative Relation Prediction |
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For each paragraph, systems must: |
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- Identify related paragraphs (by index) |
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- Assign one or more relation types: |
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- `supporting` |
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- `contradictive` |
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- `complemental` |
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- `modifying` |
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## Dataset Description |
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### Languages |
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- English |
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- French |
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### Granularity |
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- Paragraph level |
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### Splits |
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#### Training Set (parsed_data_en) |
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- Source: UN-RES dataset [Gao et al., 2025](https://aclanthology.org/2025.emnlp-demos.3/) |
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- Size: 2,695 UN resolutions |
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- Language: French (with machine-generated English translations) |
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- Annotation: paragraph-level argumentative structure |
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#### Test Set (parsed_data_fr) |
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- Source: UNESCO International Conference on Education (1934–2008) |
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- Size: 45 parsed documents (each may contain up to three resolutions in **JSON**) |
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- Language: French |
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- Annotation: paragraph-level (held out for evaluation) |
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- Validation set: none |
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#### Tags |
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- See `education_dimensions_updated.csv` |
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--- |
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## Data Format |
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All data are provided in **JSON** format following a fixed schema. |
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### Example (simplified) |
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```json |
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"TEXT_ID": "ICPE-25-1962_RES1-FR_res_54", |
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"RECOMMENDATION": 54, |
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"TITLE": "LA PLANIFICATION DE L'ÉDUCATION", |
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"METADATA": { |
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"structure": { |
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"doc_title": "ICPE-25-1962_RES1-FR", |
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"nb_paras": 58, |
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"preambular_para": [], |
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"operative_para": [] |
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"think": "" |
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} |
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}, |
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"body": { |
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"paras": [ |
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{ |
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"para_number": 1, |
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"para": "La Conférence internationale de l'instruction publique, Convoquée à...", |
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"type": null, |
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"tags": [], |
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"matched_paras": [], |
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"think": "", |
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"para_en": "The International Conference on Education, convened in ..." |
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}, |
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... |
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] |
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} |
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} |
