license: cc-by-nc-sa-4.0
source_datasets:
- extended
language_creators:
- found
language:
- bg
- cs
- da
- de
- el
- en
- es
- et
- fi
- fr
- hr
- hu
- it
- lt
- lv
- mt
- nl
- pl
- pt
- ro
- sk
- sl
- sv
tags:
- politics
size_categories:
- 10K<n<100K
pretty_name: EU Debates
Dataset Description
EU Debates is a corpus of parliamentary proceedings (debates) from the EU parliament released by Chalkidis and Brandl (2024). The corpus consists of approx. 87k individual speeches in the period 2009-2023. We exhaustively scrape the data from the official European Parliament Plenary website (Link). All speeches are time-stamped, thematically organized on debates, and include metadata relevant to the speaker's identity (full name, euro-party affiliation, speaker role), and the debate (date and title). Older debate speeches are originally in English, while newer ones are linguistically diverse across the 23 official EU languages, thus we also provide machine-translated versions in English, when official translations are missing, using the EasyNMT framework with the M2M2-100 (418M) model.
Data Fields
speaker_name: a
string` with the full-name of the speaker.speaker_party
: astring
with the name of the euro-party (group) that the MEP is affiliated with.speaker_role
: astring
with the role of speaker (Member of the European Parliament (MEP), EUROPARL President, etc.)debate_title
: astring
with the title of the debate in the European Parliament.date
: astring
with the full date (YYYY-MM-DD) of the speech.year
astring
with the year (YYYY).text
: astring
with the full speech of the speaker.translated_text
: astring
with the translation of the speech in English, if the original is not.
Data Instances
Example of a data instance from the EU Debates dataset:
{
'speaker_name': 'Annemie Neyts-Uyttebroeck'
'speaker_party': 'ALDE',
'speaker_role': 'MEP', ,
'debate_title': 'Iran (debate)',
'date': '2009-07-15',
'year': '2009',
'text': 'Iran is a large country with a large and predominantly young population, a long and eventful history and an impressive culture. [...]'
'translated_text': None
}
# How to use
```python
from datasets import load_dataset
eu_debates_dataset = load_dataset('coastalcph/eu_debates'))
Dataset Statistics
Distribution of speeches across euro-parties:
Euro-party | No. of Speeches |
EPP | 25,455 (29%) |
S&D | 20,042 (23%) |
ALDE | 8,946 (10%) |
ECR | 7,493 (9%) |
ID | 6,970 (8%) |
GUE/NGL | 6,780 (8%) |
Greens/EFA | 6,398 (7%) |
NI | 5,127 (6\%) |
Total | 87,221 |
Distribution of speeches across years and euro-parties:
Year | EPP | S&D | ALDE | ECR | ID | GUE/NGL | Greens/EFA | NI | Total |
2009 | 748 | 456 | 180 | 138 | 72 | 174 | 113 | 163 | 2044 |
2010 | 3205 | 1623 | 616 | 340 | 341 | 529 | 427 | 546 | 7627 |
2011 | 4479 | 2509 | 817 | 418 | 761 | 792 | 490 | 614 | 10880 |
2012 | 3366 | 1892 | 583 | 419 | 560 | 486 | 351 | 347 | 8004 |
2013 | 724 | 636 | 240 | 175 | 152 | 155 | 170 | 154 | 2406 |
2014 | 578 | 555 | 184 | 180 | 131 | 160 | 144 | 180 | 2112 |
2015 | 978 | 1029 | 337 | 405 | 398 | 325 | 246 | 240 | 3958 |
2016 | 919 | 972 | 309 | 387 | 457 | 317 | 225 | 151 | 3737 |
2017 | 649 | 766 | 181 | 288 | 321 | 229 | 162 | 135 | 2731 |
2018 | 554 | 611 | 161 | 242 | 248 | 175 | 160 | 133 | 2284 |
2019 | 1296 | 1339 | 719 | 556 | 513 | 463 | 490 | 353 | 5729 |
2020 | 1660 | 1564 | 823 | 828 | 661 | 526 | 604 | 346 | 7012 |
2021 | 2147 | 2189 | 1290 | 1062 | 909 | 708 | 990 | 625 | 9920 |
2022 | 2436 | 2273 | 1466 | 1177 | 827 | 962 | 1031 | 641 | 10813 |
2023 | 1716 | 1628 | 1040 | 878 | 619 | 779 | 795 | 499 | 7954 |
Citation Information
@inproceedings{chalkidis-and-brandl-eu-llama-2024,
title = "Llama meets EU: Investigating the European political spectrum through the lens of LLMs",
author = "Chalkidis, Ilias and
Stephanie Brandl",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics",
month = jun,
year = "2021",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
}