README.md

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Dataset Card for EuroParlVote

Dataset Details

Dataset Description

EuroParlVote links European Parliament debate speeches to roll-call votes and Member of European Parliament (MEP) demographic data across up to 24 official 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).

This dataset is based on publicly available EP plenary records and enhanced with metadata scraped from Wikipedia and other official sources.

• 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) (NLP): Up to 24 official EU languages (Language column)
• License: CC BY-NC 4.0 (to confirm)

Dataset Sources

• Repository: https://huggingface.co/datasets/unimelb-nlp/EuroParlVote
• Paper: Demographics and Democracy: Benchmarking LLMs’ Gender Bias and Political Leaning in European Parliament (EMNLP 2024, camera-ready)
• Demo: [EuroParlVote website]

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}
}