README.md

---
license: cc-by-nc-4.0
tags:
- legal-nlp
- multilingual
- constitutions
- machine-translation
- comparative-law
- mcwc
dataset-name: MCWC
---

# Multilingual Corpus of World’s Constitutions (MCWC)

The **MCWC** is a curated multilingual corpus of constitutional texts from **191 countries**, including both current and historical versions. The dataset provides aligned constitutional content in **English, Arabic, and Spanish**, enabling comparative legal analysis and multilingual NLP research.  

This CSV version is a cleaned, structured, and sentence-aligned representation of the corpus, suitable for machine translation, information retrieval, and a wide range of legal NLP tasks.

📄 **Paper (OSACT @ LREC-COLING 2024):**  
https://aclanthology.org/2024.osact-1.7/

---

## Contents of the dataset

`MCWC.csv` contains:

- **Constitutional text in three languages:** English, Arabic, Spanish  
- **Pairwise sentence alignments** using a stable `AlignID`  
- **Country and continent metadata**  
- **Cleaned and normalised text** for direct use in NLP pipelines  
- **Translations** for constitutions originally missing in Arabic or Spanish, generated using a fine-tuned NMT model  
- **One row per sentence**, enabling easy filtering and cross-lingual matching

Typical columns include:

- `country`  
- `continent`  
- `align_id`  
- `text_en`  
- `text_ar`  
- `text_es`  
- `constitution_year`  
- `language_source` (original vs. machine-translated)

---

## Motivation

Constitutions form the legal and philosophical foundation of nation-states. However, constitutional research is often limited by:

- fragmented access to multilingual versions  
- inconsistent formatting and metadata  
- lack of high-quality sentence alignment  
- scarcity of constitutional text in Arabic and Spanish  

The MCWC addresses these gaps by offering a unified and multilingual corpus built for computational analysis, comparative constitutional research, and machine translation development.  

The acronym **MCWC** is pronounced *“Makkuk”*, an Arabic word for *space shuttle*, chosen to symbolise the corpus’s role in enabling cross-lingual travel between legal systems.

---

## Data sources and preparation

The corpus integrates material from:

- the Comparative Constitutions Project  
- the Constitute Project  
- Wikipedia and various governmental archives  

Raw texts—primarily in XML—were normalised, cleaned, and sentence-segmented. For constitutions lacking Arabic or Spanish versions, we generated translations using a fine-tuned state-of-the-art MT model.

A custom parser aligned sentences across languages using structural cues (article numbers, section labels, etc.), with each sentence assigned a persistent `AlignID` for alignment across all three languages.

To support comparative analysis, we applied:

- continent classification via gazetteer matching  
- tokenisation and normalisation  
- vocabulary overlap statistics  
- TF-IDF cosine-similarity analysis (English)  

The paper provides further detail, including heatmaps of by-continent overlap and cross-lingual similarity.

---

## Dataset statistics

- **223 constitutions**  
- **191 countries**  
- **95 constitutions available in all three languages**  
- **52,177 aligned English–Arabic sentences**  
- **48,892 aligned English–Spanish sentences**  
- **27,352 aligned Arabic–Spanish sentences**  
- **236,156 parallel machine-generated sentences** (SeamlessM4T-v2)

Across continents:

- Africa: 65 constitutions  
- Asia: 54  
- Europe: 49  
- North America: 26  
- South America: 15  
- Oceania: 14  

Average lengths and TTR values are reported in the paper.

---

## Intended uses

The MCWC is designed to support:

- multilingual machine translation  
- legal text analysis and retrieval  
- constitutional comparison and modelling  
- multilingual topic modelling  
- cross-lingual semantic similarity  
- legal corpus linguistics  
- training/testing MT systems for legal text  

Users may also employ the dataset to study variation in legal language across jurisdictions and historical periods.

---

## Translation and evaluation

For missing Arabic/Spanish texts, English constitutions were translated using **Facebook’s Seamless-m4t-v2-large**.  

Evaluation included:

- **BLEU = 0.68** on 500 manually inspected En–Ar and En–Es pairs  
- **Human annotation** by two Arabic NLP experts  
  - Cohen’s κ = 0.30 (due to label imbalance)  
  - Krippendorff’s α ≈ 0.90 (robust reliability)  

Fine-tuned Marian NMT models trained on the MCWC consistently improved translation quality across all six language pairs. These models are available on HuggingFace.

---
### Train / Validation / Test Splits

The original MCWC paper does not define an official train–validation–test split.  
To support reproducibility and facilitate machine-learning experiments, we provide a recommended **80/10/10** split using a fixed random seed. This ensures consistent partitioning whilst keeping the dataset fully shuffled.

The split is applied over the complete CSV file (`MCWC.csv`) and does not use stratification, as constitutional texts vary considerably across countries and languages.

```python
import pandas as pd
from sklearn.model_selection import train_test_split

# Load the full corpus
df = pd.read_csv("MCWC.csv")

# First split: train vs temporary (validation + test)
train_df, temp_df = train_test_split(
    df,
    test_size=0.2,     # 20% goes to val+test
    random_state=42,
    shuffle=True
)

# Second split: create separate validation and test sets
val_df, test_df = train_test_split(
    temp_df,
    test_size=0.5,     # half of 20% → 10% each
    random_state=42,
    shuffle=True
)

# Save the splits
train_df.to_csv("MCWC_train.csv", index=False)
val_df.to_csv("MCWC_val.csv", index=False)
test_df.to_csv("MCWC_test.csv", index=False)

print("Train:", len(train_df))
print("Validation:", len(val_df))
print("Test:", len(test_df))
```

---
 
## Ethical considerations and limitations

- Source texts originate from open datasets (CCP, Constitute Project) and governmental archives.  
- This CSV is a *processed* and *aligned* derivative, not a redistribution of the original XML files.  
- Translation quality varies depending on source material and MT system limitations.  
- Some constitutions contain archaic, historical, or culturally-specific legal phrasing that may not translate literally.  
- Differences in translation providers (HeinOnline, IDEA, OUP, etc.) may introduce stylistic or interpretative variance.

Users requiring original XML files should retrieve them directly from the Constitute Project.

---

## Citation

If you use this dataset, please cite:

**El-Haj, M. & Ezzini, S. (2024).**  
*The Multilingual Corpus of World’s Constitutions (MCWC).*  
OSACT Workshop @ LREC-COLING 2024.  
https://aclanthology.org/2024.osact-1.7/

---

## Contact

For questions or collaboration:

**Mo El-Haj**  
UCREL NLP Group  
Lancaster University & VinUniversity  
Email: m.el-haj@lancaster.ac.uk / elhaj.m@vinuni.edu.vn