  West African Pidgin English is a language that is significantly spoken in
West Africa, consisting of at least 75 million speakers. Nevertheless, proper
machine translation systems and relevant NLP datasets for pidgin English are
virtually absent. In this work, we develop techniques targeted at bridging the
gap between Pidgin English and English in the context of natural language
generation. %As a proof of concept, we explore the proposed techniques in the
area of data-to-text generation. By building upon the previously released
monolingual Pidgin English text and parallel English data-to-text corpus, we
hope to build a system that can automatically generate Pidgin English
descriptions from structured data. We first train a data-to-English text
generation system, before employing techniques in unsupervised neural machine
translation and self-training to establish the Pidgin-to-English cross-lingual
alignment. The human evaluation performed on the generated Pidgin texts shows
that, though still far from being practically usable, the pivoting +
self-training technique improves both Pidgin text fluency and relevance.
