README.md

metadata

language:
  - de
  - fr
  - it
  - rm
license: cc-by-4.0
task_categories:
  - text-classification
task_ids:
  - multi-label-classification
pretty_name: Swiss4Arg- Argument Mining on Swiss Federal Voting Brochures
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/default/train.parquet
  - config_name: annotations
    data_files:
      - split: train
        path: data/annotations/train.parquet

Swiss4Arg: Argument Mining

A multilingual argument mining dataset built from Swiss federal voting brochures in German, French, Italian, and Romansh. The dataset covers 53 federal proposals and provides argument component labels and argumentation structure (relation + target) annotated by 12 human annotators (3 per language), consolidated via majority voting.

Dataset Configurations

default

Long format: one row per segment per language (~12k rows). Each segment appears four times (once per language) with the shared majority-vote label.

from datasets import load_dataset
ds = load_dataset("---/swiss-argument-mining")
# filter one language
de = ds["train"].filter(lambda x: x["language"] == "de")

Columns

Column	Description
proposal_id	Federal proposal identifier
language	de / fr / it / rm
side	pro or con section of the brochure
id	Segment ID within document
chapter_type	major claim / Pro / Con
text	Segment text in the given language
component	Major Claim / Claim / Premise / Non-Argumentative
relation	Support / Attack / null
target	Target segment (e.g. Claim 4, Major Claim) / null

annotations

Wide format: one row per segment (~3k rows) with all 12 individual annotator columns alongside the majority-vote labels. Use this for inter-annotator agreement research or disagreement analysis.

ds = load_dataset("---/swiss-argument-mining", "annotations")

Columns: proposal_id, side, id, chapter_type, level, text_de/fr/it/rm, component_{lang}_{1-3} × 12, relation_{lang}_{1-3} × 12, target_{lang}_{1-3} × 12, majority_component, majority_relation, majority_target.

Annotation Schema

Following Stab & Gurevych (2017), the argumentative hierarchy is:

Premise ──supports/attacks──▶ Claim ──supports/attacks──▶ Major Claim

• Major Claim: central thesis of the brochure (one per document, pre-annotated)
• Claim: debatable sub-position directly targeting the major claim
• Premise: evidence or fact supporting/attacking a specific claim
• Non-Argumentative: headers, procedural text, neutral background

Dataset Statistics

Proposals	53
Languages	German, French, Italian, Romansh
Annotators	12 (3 per language)
Total segments	2,987
Premises	1,795 (60.1%)
Claims	788 (26.4%)
Non-Argumentative	298 (10.0%)
Major Claims	106 (3.5%)

Citation

@article{coming-soon,
  title   = {coming soon},
}