  Nigerian Pidgin is an English-derived contact language and is traditionally
an oral language, spoken by approximately 100 million people. No orthographic
standard has yet been adopted, and thus the few available Pidgin datasets that
exist are characterised by noise in the form of orthographic variations. This
contributes to under-performance of models in critical NLP tasks. The current
work is the first to describe various types of orthographic variations commonly
found in Nigerian Pidgin texts, and model this orthographic variation. The
variations identified in the dataset form the basis of a phonetic-theoretic
framework for word editing, which is used to generate orthographic variations
to augment training data. We test the effect of this data augmentation on two
critical NLP tasks: machine translation and sentiment analysis. The proposed
variation generation framework augments the training data with new orthographic
variants which are relevant for the test set but did not occur in the training
set originally. Our results demonstrate the positive effect of augmenting the
training data with a combination of real texts from other corpora as well as
synthesized orthographic variation, resulting in performance improvements of
2.1 points in sentiment analysis and 1.4 BLEU points in translation to English.
