  Despite attempts to make Large Language Models multi-lingual, many of the
world's languages are still severely under-resourced. This widens the
performance gap between NLP and AI applications aimed at well-financed, and
those aimed at less-resourced languages. In this paper, we focus on Nigerian
Pidgin (NP), which is spoken by nearly 100 million people, but has
comparatively very few NLP resources and corpora. We address the task of
Implicit Discourse Relation Classification (IDRC) and systematically compare an
approach translating NP data to English and then using a well-resourced IDRC
tool and back-projecting the labels versus creating a synthetic discourse
corpus for NP, in which we translate PDTB and project PDTB labels, and then
train an NP IDR classifier. The latter approach of learning a "native" NP
classifier outperforms our baseline by 13.27\% and 33.98\% in f$_{1}$ score for
4-way and 11-way classification, respectively.
