README.md

---
license: apache-2.0
task_categories:
- text-retrieval
size_categories:
- 10K<n<100K
language:
- en
- de
- fr
- it
- es
- pl
- ro
- nl
- el
- hu
- pt
- cs
- sv
- bg
- da
- fi
- sk
- lt
- hr
- sl
- et
- lv
- mt
- ga
pretty_name: multi_eup
configs:
- config_name: default
  data_files:
  - split: full
    path:
    - "MultiEuP.csv"
---

## NOTES FOR DOWNLOAD!

1. Highly recommend downloading it via the API:

```bash
curl -X GET \
     "https://datasets-server.huggingface.co/first-rows?dataset=unimelb-nlp%2FMulti-EuP&config=default&split=full"
```

2. If you are using the HuggingFace library, please follow these steps:
```bash
pip install datasets
```
```python
from datasets import load_dataset

dataset = load_dataset("unimelb-nlp/Multi-EuP", keep_default_na=False)
```

Note: It's crucial to use **keep_default_na=False** because some datasets contain 'null' values, such as qid_GA, due to the Irish (GA) debate titles not being published before it became an official EU language on 1 January 2022. Additionally, some debate text may not belong to the active 705 MEP, resulting in missing matching information.

### Dataset Description

- **Homepage:**
- **Repository:** [Multi-EuP Dataset repository](https://github.com/jrnlp/Multi-EuP)
- **Paper:** [Multi-EuP: The Multilingual European Parliament Dataset for Analysis of Bias in Information Retrieval](https://arxiv.org/pdf/2311.01870.pdf)
- **Leaderboard:** [Papers with Code leaderboard for Multi-EuP](Coming soon)
- **Point of Contact:** [Jinrui Yang](mailto:jinruiy@student.unimelb.edu.au)

### Dataset Summary

The Multi-Eup is a new multilingual benchmark dataset, comprising 22K multilingual documents collected from the European Parliament, spanning 24 languages. This dataset is designed to investigate fairness in a multilingual information retrieval (IR) context to analyze both language and demographic bias in a ranking context. It boasts an authentic multilingual corpus, featuring topics translated into all 24 languages, as well as cross-lingual relevance judgments. Furthermore, it offers rich demographic information associated with its documents, facilitating the study of demographic bias. 

### Dataset statistics

| Language | ISO code | Countries where official lang. | Native Usage | Total Usage | # Docs | Words per Doc (mean/median) |
|----------|----------|--------------------------------|--------------|-------------|-------|------------------------------|
| English  | EN       | United Kingdom, Ireland, Malta  | 13%          | 51%         | 7123  | 286/200                      |
| German   | DE       | Germany, Belgium, Luxembourg    | 16%          | 32%         | 3433  | 180/164                      |
| French   | FR       | France, Belgium, Luxembourg      | 12%          | 26%         | 2779  | 296/223                      |
| Italian  | IT       | Italy                            | 13%          | 16%         | 1829  | 190/175                      |
| Spanish  | ES       | Spain                            | 8%           | 15%         | 2371  | 232/198                      |
| Polish   | PL       | Poland                           | 8%           | 9%          | 1841  | 155/148                      |
| Romanian | RO       | Romania                          | 5%           | 5%          | 794   | 186/172                      |
| Dutch    | NL       | Netherlands, Belgium             | 4%           | 5%          | 897   | 184/170                      |
| Greek    | EL       | Greece, Cyprus                   | 3%           | 4%          | 707   | 209/205                      |
| Hungarian| HU       | Hungary                          | 3%           | 3%          | 614   | 126/128                      |
| Portuguese| PT      | Portugal                         | 2%           | 3%          | 1176  | 179/167                      |
| Czech    | CS       | Czech Republic                   | 2%           | 3%          | 397   | 167/149                      |
| Swedish  | SV       | Sweden                           | 2%           | 3%          | 531   | 175/165                      |
| Bulgarian| BG       | Bulgaria                         | 2%           | 2%          | 408   | 196/178                      |
| Danish   | DA       | Denmark                          | 1%           | 1%          | 292   | 218/198                      |
| Finnish  | FI       | Finland                          | 1%           | 1%          | 405   | 94/87                        |
| Slovak   | SK       | Slovakia                         | 1%           | 1%          | 348   | 151/158                      |
| Lithuanian| LT     | Lithuania                       | 1%           | 1%          | 115   | 142/127                      |
| Croatian | HR      | Croatia                          | <1%          | <1%         | 524   | 183/164                      |
| Slovene  | SL      | Slovenia                         | <1%          | <1%         | 270   | 201/163                      |
| Estonian | ET      | Estonia                          | <1%          | <1%         | 58    | 160/158                      |
| Latvian  | LV      | Latvia                           | <1%          | <1%         | 89    | 111/123                      |
| Maltese  | MT      | Malta                            | <1%          | <1%         | 178   | 117/115                      |
| Irish    | GA      | Ireland                          | <1%          | <1%         | 33    | 198/172                      |
*Table 1: Multi-EuP statistics, broken down by language: ISO language code; EU member states using the language officially; proportion of the EU population speaking the language; number of debate speech documents in Mult-EuP; and words per document (mean/median).*

## Dataset Structure

The Multi-EuP dataset contains two files, debate coprpus<https://huggingface.co/datasets/unimelb-nlp/Multi-EuP/blob/main/Debates.csv> and MEP info <https://huggingface.co/datasets/unimelb-nlp/Multi-EuP/blob/main/MEPs.csv>. The MEP id in two files can be used for alignment.


### Debate Corpus Fileds

The debate instance and attributes are displayed below. See the [Multi-EuP debate viewer](https://huggingface.co/datasets/unimelb-nlp/Multi-EuP/viewer/default/train) to explore more examples.

- `TEXT`: A string representing the content of the debate speech.
- `NAME`: A string containing the name of the MEP who presented the speech.
- `PRESIDENT`: A boolean indicating whether the MEP is the president (typically discussing procedural matters to introduce the debate).
- `MEPID`: An integer representing the unique ID of the MEP in the EU.
- `LANGUAGE`: The language ISO code of the text.
- `PARTY`: A string representing the political party of the MEP.
- `TEXTID`: A hash string serving as a unique identifier for the speech text.
- `CODICT`: An integer serving as the unique identifier for the speech text.
- `DATE`: A string indicating the date when the debate happened.
- `VOD-START`: The timestamp of the speech start.
- `VOD-END`: The timestamp of the speech end.
- `title_X`: A string representing the title in language X (e.g., `title_EN`). Note that this field might be empty for some languages, such as GA, as the EU does not publish titles in Irish (GA).
- `did`: A string representing the unique ID of the text (e.g., `doc0`, `doc1`).
- `qid_X`: A string representing the unique ID of the title in language X (e.g., `qid0#EN`).





### MEP info Fileds

The information dictionary for the 705 MEPs was constructed as follows:

- `fullName`: A string representing the full name of the MEP.
- `politicalGroup`: A string indicating the political group affiliation of the MEP.
- `id`: An integer representing the unique identifier of the MEP in the EU.
- `nationalPoliticalGroup`: A string denoting the national political group of the MEP.
- `photo`: A .jpg file containing the profile picture of the MEP.
- `nameAudio`: A .mp3 file with the pronunciation of the MEP's name.
- `gender_Wiki`: A string specifying the gender of the MEP as mentioned on Wikipedia.
- `gender_2017`: A string indicating the gender of the MEP according to europal-2017(<https://aclanthology.org/E17-1101.pdf>).
- `gender`: A string representing the MEP's gender after cross-referencing information from Wikipedia, europal-2017, and manual verification.
- `dateOfBirth_Wiki`: A string stating the date of birth of the MEP as mentioned on Wikipedia.
- `dateOfBirth_Home`: A string indicating the date of birth of the MEP as found on their homepage in the EU.
- `dateOfBirth`: A string representing the date of birth of the MEP after combining information from Wikipedia, their homepage, and manual verification.
- `placeOfBirth`: A string indicating the place of birth of the MEP as mentioned on their homepage.
- `country`: A string representing the nationality country of the MEP as mentioned on their homepage.
- `homePage`: A string providing the link to the MEP's homepage.

### Data Source

This Multi-Eup dataset was collected from European Parliament (<https://www.europarl.europa.eu/portal/en>).

#### Initial Data Collection and Normalization

The code for the EMNLP MRL version is made publicly available by Jinrui Yang, Timothy Baldwin and Trevor Cohn of The University of Melbourne at <https://github.com/jrnlp/Multi-EuP>. This research was funded by Melbourne Research Scholarship and undertaken using the LIEF HPCGPGPU Facility hosted at the University of Melbourne. This facility was established with the assistance of LIEF Grant LE170100200. 

### Ethics Statement

The dataset contains publicly-available EP data that does not include personal or sensitive information, with the exception of information relating to public officeholders, e.g., the names of the active members of the European Parliament, European Council, or other official administration bodies. The collected data is licensed under the Creative Commons Attribution 4.0 International licence. <https://eur-lex.europa.eu/content/legal-notice/legal-notice.html>

### Citation Information

```
@inproceedings{yang-etal-2023-multi-eup,
    title = "Multi-{E}u{P}: The Multilingual {E}uropean Parliament Dataset for Analysis of Bias in Information Retrieval",
    author = "Yang, Jinrui  and
      Baldwin, Timothy  and
      Cohn, Trevor",
    editor = "Ataman, Duygu",
    booktitle = "Proceedings of the 3rd Workshop on Multi-lingual Representation Learning (MRL)",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.mrl-1.21",
    doi = "10.18653/v1/2023.mrl-1.21",
    pages = "282--291",
}
```