license: cc-by-4.0
Eng-PidginEdu Dataset
Overview
Eng-PidginEdu is an English–Nigerian Pidgin parallel corpus developed to support machine translation, multilingual NLP, and educational accessibility research for low-resource African languages.
The dataset contains 26,232 parallel sentence pairs collected across 8 Nigerian secondary school subjects, comprising approximately 1.09 million total tokens with high-quality sentence-level alignment and human-validated translations.
Nigerian Pidgin serves as a major lingua franca spoken across Nigeria and West Africa. This dataset was created to improve access to educational technologies and language resources for Pidgin-speaking communities.
Dataset Composition
Statistics
| Metric | Value |
|---|---|
| Total Sentence Pairs | 26,232 |
| English Tokens | 555,398 |
| Pidgin Tokens | 533,657 |
| Avg English Sentence Length | 21.17 tokens |
| Avg Pidgin Sentence Length | 20.34 tokens |
| English TTR | 0.1032 |
| Pidgin TTR | 0.0883 |
Dataset Splits
| Split | Size |
|---|---|
| Train | 20,985 |
| Validation (Dev) | 2,623 |
| Test | 2,624 |
The dataset was randomly shuffled using a fixed seed (42) to ensure reproducibility.
Languages
| Role | Language | Code |
|---|---|---|
| Source | English | en |
| Target | Nigerian Pidgin | pcm |
Data Collection Process
The dataset was compiled from publicly accessible Nigerian secondary school educational materials and curriculum-aligned learning resources.
The data preparation pipeline included:
Document extraction using:
PyPDF2python-docx
Sentence segmentation using:
- NLTK Punkt tokenizer
Text normalization and cleaning using:
- spaCy
- textacy
- regular-expression filtering
Translation generation using:
Davlan/mt5-small-en-pcm
Human validation and correction by native Nigerian Pidgin speakers
Alignment verification and post-processing
Key Features
- Large-scale English–Pidgin educational parallel corpus
- Coverage across multiple academic subjects
- Human-validated translations
- Natural Nigerian Pidgin expressions and code-switching
- Suitable for low-resource machine translation research
- Reproducible train/dev/test splits
Intended Use
This dataset is intended for:
- Machine Translation (MT)
- Low-resource NLP research
- Educational AI systems
- Cross-lingual learning applications
- Multilingual language modeling
- Evaluation of African language translation systems
Limitations
- Educational domain only
- Uneven subject distribution
- Contains orthographic variation in Nigerian Pidgin
- Includes informal and code-switched language patterns
- Does not represent all regional varieties of Nigerian Pidgin
Bias and Ethical Considerations
The dataset reflects naturally occurring educational language and may contain:
- Curriculum-specific biases
- Subject imbalance
- Linguistic variation associated with Nigerian Pidgin usage
No personally identifiable or sensitive information is included.
Glossary and Terminology Resource
In addition to the parallel sentence corpus, Eng-PidginEdu includes a bilingual educational glossary designed to improve terminology consistency and domain adaptation for machine translation systems.
Glossary Statistics
| Metric | Value |
|---|---|
| Total Glossary Entries | 13,634 |
| Source Language | English |
| Target Language | Nigerian Pidgin |
Glossary Structure
Each glossary entry contains:
| Field | Description |
|---|---|
Technical_terms |
Educational or domain-specific English terminology |
Literal meaning |
Standard English explanation or definition |
pidgin meaning |
Nigerian Pidgin translation or interpretation |
Example Entries
| Technical Term | Literal Meaning | Pidgin Meaning |
|---|---|---|
| abandoned | Having been deserted or left | Leave am / throw away |
| abandonment | The act of giving something up | Di act of giving something up |
| abbreviated | Shortened form of something | Abbreviated na short form |
Purpose of the Glossary
The glossary was developed to:
- Improve translation consistency across subjects
- Support educational terminology alignment
- Reduce mistranslation of technical concepts
- Assist low-resource terminology grounding
- Improve domain adaptation for MT systems
The glossary was also used during post-processing and human validation to maintain consistency in subject-specific translations across Computer Science, Government, Biology, Business Studies, and related domains.
Intended Use of the Glossary
The glossary can be used for:
- Terminology-aware machine translation
- Lexicon-constrained decoding
- Educational chatbot systems
- Bilingual terminology extraction
- Low-resource lexicon research
- Prompt engineering for LLM translation systems
Notes
- Some entries include naturally occurring Nigerian Pidgin paraphrases rather than strict literal translations.
- Orthographic variation may occur due to non-standardized spelling conventions in Nigerian Pidgin.
- Certain educational concepts may have multiple valid Pidgin renderings depending on context.
Licensing
This dataset is released under the CC-BY-4.0 License.
Citation
If you use this dataset, please cite:
@dataset{eng_pidginedu_2026,
author = {Oladipupo, F.},
title = {Eng-PidginEdu: An English–Nigerian Pidgin Educational Parallel Corpus},
year = {2026},
publisher = {Hugging Face}
}