---
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 <jinruiy@student.unimelb.edu.au>
---

# 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](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](https://parlvote-demo.vercel.app/)] 

## 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:**
```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}
}