# Towards Supervised and Unsupervised Neural Machine Translation Baselines for Nigerian Pidgin

arXiv: https://arxiv.org/abs/2003.12660 · HTML: https://mlsanity.com/paper/2003.12660/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2020.

## Built on by (most cited first, 8 of 8)

- [What a Creole Wants, What a Creole Needs](https://mlsanity.com/paper/2206.00437/) (2022)
- [On Language Models for Creoles](https://mlsanity.com/paper/2109.06074/) (2021)
- [Intriguing Properties of Compression on Multilingual Models](https://mlsanity.com/paper/2211.02738/) (2022)
- [How Good are Commercial Large Language Models on African Languages?](https://mlsanity.com/paper/2305.06530/) (2023)
- [Implicit Discourse Relation Classification For Nigerian Pidgin](https://mlsanity.com/paper/2406.18776/) (2024)
- [Low-Resource Cross-Lingual Adaptive Training for Nigerian Pidgin](https://mlsanity.com/paper/2307.00382/) (2023)
- [Limitations of Religious Data and the Importance of the Target Domain: Towards Machine Translation for Guinea-Bissau Creole](https://mlsanity.com/paper/2504.02674/) (2025)
- [Bridging the Gap with Retrieval-Augmented Generation: Making Prosthetic Device User Manuals Available in Marginalised Languages](https://mlsanity.com/paper/2506.23958/) (2025)

## Abstract (arXiv)

Nigerian Pidgin is arguably the most widely spoken language in Nigeria. Variants of this language are also spoken across West and Central Africa, making it a very important language. This work aims to establish supervised and unsupervised neural machine translation (NMT) baselines between English and Nigerian Pidgin. We implement and compare NMT models with different tokenization methods, creating a solid foundation for future works.