---
pretty_name: "Multi-EuP v2: European Parliament Debates with MEP Metadata (24 languages)"
dataset_name: multi-eup-v2
configs:
  - config_name: default
    data_files: "clean_all_with_did_qid.MEP.csv"
license: cc-by-4.0
multilinguality: multilingual
task_categories:
  - text-classification
  - text-retrieval
  - text-generation
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
size_categories:
  - 10K<n<100K
homepage: ""
repository: ""
paper: "https://aclanthology.org/2024.mrl-1.23/"
tags:
  - multilingual
  - european-parliament
  - political-discourse
  - metadata
  - mep
---
# Multi-EuP-v2

This dataset card documents **Multi-EuP-v2**, a multilingual corpus of European Parliament debate speeches enriched with Member of European Parliament (MEP) metadata and multilingual debate titles/IDs. It supports research on political text analysis, speaker-attribute prediction, stance/vote prediction, multilingual NLP, and retrieval.

## Dataset Details

### Dataset Description

**Multi-EuP-v2** aggregates **50,337** debate speeches (each a unique `did`) in **24 languages**. Each row contains the speech text (`TEXT`), speaker identity (`NAME`, `MEPID`), language (`LANGUAGE`), political group (`PARTY`), country and gender of the MEP, date, video timestamps, plus **multilingual debate titles `title_<LANG>`** and **per-language debate/vote linkage IDs `qid_<LANG>`**.

- **Curated by:** Jinrui Yang, Fan Jiang, Timothy Baldwin  
- **Funded by:** Melbourne Research Scholarship; LIEF HPC-GPGPU Facility (LE170100200)  
- **Shared by:** University of Melbourne 
- **Language(s) (NLP):** `bg, cs, da, de, el, en, es, et, fi, fr, ga, hr, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv` (24 total)  
- **License:** cc-by-4.0

### Dataset Sources

- **Repository:** [https://github.com/jrnlp/MLIR_language_bias]  
- **Paper:** https://aclanthology.org/2024.mrl-1.23/  

## Uses

### Direct Use
- **Text classification:** predict `gender`, `PARTY`, or `country` from `TEXT`.  
- **Stance/vote prediction:** link `qid_<LANG>` to external roll-call vote labels.  
- **Multilingual representation learning:** train/evaluate models across 24 EU languages.  
- **Information retrieval:** index `TEXT` and use `title_*`/`qid_*` as multilingual query anchors.

### Out-of-Scope Use
- Inferring private attributes beyond public MEP metadata.  
- Automated profiling for sensitive decisions.  
- Misrepresenting model outputs as factual statements.

## Dataset Structure

Each row corresponds to a single speech/document.

**Core fields:**
- `did` *(string)* — unique speech ID  
- `TEXT` *(string)* — speech text  
- `DATE` *(string/date)* — debate date  
- `LANGUAGE` *(string)* — language code  
- `NAME` *(string)* — MEP name  
- `MEPID` *(string)* — MEP ID  
- `PARTY` *(string)* — political group  
- `country` *(string)* — MEP's country  
- `gender` *(string)* — `Female`, `Male`, or `Unknown`  
- Additional provenance fields: `PRESIDENT`, `TEXTID`, `CODICT`, `VOD-START`, `VOD-END`

**Multilingual metadata:**
- `title_<LANG>` *(string)* — debate title in that language  
- `qid_<LANG>` *(string)* — debate/vote linkage ID in that language

**Splits:** Single CSV, no predefined splits.

**Basic stats:**
- Rows: 50,337  
- Languages: 24  
- Top political groups: PPE 8,869; S-D 8,468; Renew 5,313; ECR 4,130; Verts/ALE 4,001; ID 3,286; The Left 2,951; NI 2,539; GUE/NGL 468  
- Gender counts: Female 25,536; Male 23,461; Unknown 349  
- Top countries: Germany 7,226; France 6,158; Poland 3,706; Spain 3,312; Italy 3,222; Netherlands 1,924; Greece 1,756; Romania 1,701; Czechia 1,661; Portugal 1,150; Belgium 1,134; Hungary 1,106

## Dataset Creation

### Curation Rationale
Support multilingual political text research, enabling standardized tasks in gender/group prediction, stance/vote prediction, and IR.

### Source Data

#### Data Collection and Processing
- **Source:** Official EP debates.  
- **Processing:** metadata linking, language verification, deduplication, multilingual title extraction.  
- **Quality checks:** consistency in language tags and IDs.

#### Who are the source data producers?
MEPs speaking in plenary debates; titles from official EP records.

### Annotations

#### Annotation process
Metadata compiled from public records; no manual stance labels.

#### Personal and Sensitive Information
Contains names and political opinions of public officials.

## Bias, Risks, and Limitations
- Domain bias: formal political discourse.  
- Risk in demographic inference tasks.  
- Language/script differences affect comparability.

### Recommendations
- Report per-language metrics.  
- Avoid over-claiming causal interpretations.

## Citation

**BibTeX:**
```bibtex
@inproceedings{yang-etal-2024-language-bias,
  title     = {Language Bias in Multilingual Information Retrieval: The Nature of the Beast and Mitigation Methods},
  author    = {Yang, Jinrui and Jiang, Fan and Baldwin, Timothy},
  booktitle = {Proceedings of the Fourth Workshop on Multilingual Representation Learning (MRL 2024)},
  year      = {2024},
  pages     = {280--292},
  publisher = {Association for Computational Linguistics},
  url       = {https://aclanthology.org/2024.mrl-1.23/},
  doi       = {10.18653/v1/2024.mrl-1.23}
}
```

**APA:** Yang, J., Jiang, F., & Baldwin, T. (2024). *Language Bias in Multilingual Information Retrieval: The Nature of the Beast and Mitigation Methods*. In MRL 2024. ACL.

## Contact
jinrui.yang@student.unimelb.edu.au