  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.
