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

• 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}
}