Datasets:
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README.md
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It supports two primary benchmark tasks:
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1. **Gender Classification**: Predict the MEP’s gender from a debate speech.
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2. **Vote Prediction**: Predict a FOR/AGAINST vote from the topic and speech (optionally with demographic context).
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- **Curated by:** Jinrui Yang, Xudong Han, Timothy Baldwin
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- **Funded by:** Melbourne Research Scholarship; LIEF HPC-GPGPU Facility (LE170100200)
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- **Shared by:** University of Melbourne
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- **Language(s)
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- **License:** CC BY-NC 4.0
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### Dataset Sources
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dataset_name: europarlvote
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pretty_name: EuroParlVote
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paperswithcode_id: null
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task_categories:
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- text-classification
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tasks:
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- gender-classification
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- stance-detection
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- vote-prediction
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multilinguality: multilingual
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language:
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- bg
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- cs
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- da
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- de
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- el
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- en
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- es
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- et
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- fi
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- fr
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- ga
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- hr
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- hu
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- it
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- lt
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- lv
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- mt
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- nl
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- pl
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- pt
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- ro
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- sk
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- sl
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- sv
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license: cc-by-nc-4.0
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size_categories:
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- 10K<n<100K
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annotations_creators:
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- expert-generated
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source_datasets:
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- original
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domain:
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- politics
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- public-policy
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demographics:
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- adult
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- public-figures
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creators:
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- Jinrui Yang
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- Xudong Han
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- Timothy Baldwin
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maintainers:
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- Jinrui Yang <[email protected]>
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---
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# EuroParlVote
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EuroParlVote links **European Parliament debate speeches** to **roll-call votes** and **MEP demographics** (gender, age, country, political group) across up to **24 EU languages**.
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It supports two primary benchmark tasks:
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1. **Gender Classification** – predict the MEP’s gender from a debate speech.
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2. **Vote Prediction** – predict a FOR/AGAINST vote from the topic and speech (optionally with demographic context).
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## Dataset Details
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- **Curated by:** Jinrui Yang, Xudong Han, Timothy Baldwin
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- **Funded by:** Melbourne Research Scholarship; LIEF HPC-GPGPU Facility (LE170100200)
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- **Shared by:** University of Melbourne
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- **Language(s):** see list above (`language`)
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- **License:** CC BY-NC 4.0
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### Dataset Sources
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