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EuroParlVote

Tasks:
Text Classification
Modalities:
Tabular
Text
Formats:
csv
Languages:
Bulgarian
Czech
Danish
Size:
10K - 100K
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metadata
dataset_name: europarlvote
pretty_name: EuroParlVote
paperswithcode_id: null
task_categories:
  - text-classification
tasks:
  - gender-classification
  - stance-detection
  - vote-prediction
multilinguality: multilingual
language:
  - bg
  - cs
  - da
  - de
  - el
  - en
  - es
  - et
  - fi
  - fr
  - ga
  - hr
  - hu
  - it
  - lt
  - lv
  - mt
  - nl
  - pl
  - pt
  - ro
  - sk
  - sl
  - sv
license: cc-by-nc-4.0
size_categories:
  - 10K<n<100K
annotations_creators:
  - expert-generated
source_datasets:
  - original
domain:
  - politics
  - public-policy
demographics:
  - adult
  - public-figures
creators:
  - Jinrui Yang
  - Xudong Han
  - Timothy Baldwin
maintainers:
  - Jinrui Yang <[email protected]>

EuroParlVote

EuroParlVote links European Parliament debate speeches to roll-call votes and MEP demographics (gender, age, country, political group) across up to 24 EU languages.

It supports two primary benchmark tasks:

  1. Gender Classification – predict the MEP’s gender from a debate speech.
  2. Vote Prediction – predict a FOR/AGAINST vote from the topic and speech (optionally with demographic context).

Dataset Details

  • Curated by: Jinrui Yang, Xudong Han, Timothy Baldwin
  • Funded by: Melbourne Research Scholarship; LIEF HPC-GPGPU Facility (LE170100200)
  • Shared by: University of Melbourne
  • Language(s): see list above (language)
  • License: CC BY-NC 4.0

Dataset Sources

Uses

Direct Use

  • Benchmark LLM fairness/bias in political discourse.
  • Multilingual political text classification and vote prediction.
  • Study demographic effects (gender, group) on model behavior.

Out-of-Scope Use

  • Real-time vote forecasting or influencing political processes.
  • Targeting individuals or groups.
  • Disinformation or harassment.

Dataset Structure

File Structure

The dataset is split into train, dev, and test (~8:1:1).
This example shows the dev_set.csv structure.

Columns

Column Type Description
Chapter float Debate chapter number
Chapter_ID string Debate chapter unique identifier
Act_ID string Legislative act ID (may be "MISSING")
Report_ID string Parliamentary report ID
Debate_ID string Unique debate ID + language suffix
Vote_ID int Unique roll-call vote ID
Vote_Description string English description of the vote topic
Vote_Timestamp string Date-time of the vote
Language string ISO language code of the speech
Speaker string Speaker’s full name
MEP_ID int Unique MEP identifier
Party string Party affiliation (if available)
Role string Role in debate (e.g., rapporteur)
CODICT int Speaker unique code
Speaker_Type string Type of speaker (e.g., MEP, Chair)
Start_Time string Start time (uniform in this split)
End_Time string End time (uniform in this split)
Title_[XX] string Debate title in language XX (24 variants, e.g., Title_EN, Title_FR, Title_DE)
Speech string Full debate speech text
position string Vote label: FOR / AGAINST
country_code_x string Country code (original source)
group_code string Political group code (8 possible)
first_name string MEP first name
last_name string MEP last name
country_code_y string Country code (from demographic scrape)
date_of_birth string Date of birth (YYYY-MM-DD)
email string Public MEP email (if available)
facebook string Facebook profile URL (if available)
twitter string Twitter/X profile URL (if available)
gender string Binary label: MALE / FEMALE

Note: Title columns cover all official EU languages; Speech is in the original debate language (Language).

Label Distribution (dev split)

  • position: FOR and AGAINST are balanced in dev/test.
  • gender: MALE, FEMALE.

Dataset Creation

Curation Rationale

Existing multilingual political datasets rarely link actual speeches to real-world vote outcomes and demographics, making fairness and bias studies difficult. This dataset bridges that gap.

Source Data

  • Votes from HowTheyVote.eu.
  • Debates aligned via vote metadata references.
  • Demographics from Wikipedia & official EP sources.

Processing

  • Removed abstentions & missing topic/speech.
  • Gender inferred from pronouns and manually checked.

Annotation

  • Gender labels created via semi-automatic heuristics, with manual validation.
  • Vote labels come directly from official roll-call data.

Sensitive Information

  • Contains names, countries, political groups of public figures (MEPs).
  • Binary gender labels do not reflect all identities.

Bias, Risks, and Limitations

  • Binary gender assumption.
  • Political group may not fully capture ideology.
  • Translation hurts performance; originals recommended.
  • Biases in speeches may reflect political context, not individual ideology.

Citation

BibTeX:

@inproceedings{yang2024europarlvote,
  title={Demographics and Democracy: Benchmarking LLMs’ Gender Bias and Political Leaning in European Parliament},
  author={Yang, Jinrui and Han, Xudong and Baldwin, Timothy},
  booktitle={Proceedings of the 8th International Conference on Natural Language and Speech Processing},
  year={2025}
}