Create README.md

README.md

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+---
+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
+[email protected]