  The contrast between the need for large amounts of data for current Natural
Language Processing (NLP) techniques, and the lack thereof, is accentuated in
the case of African languages, most of which are considered low-resource. To
help circumvent this issue, we explore techniques exploiting the qualities of
morphologically rich languages (MRLs), while leveraging pretrained word vectors
in well-resourced languages. In our exploration, we show that a meta-embedding
approach combining both pretrained and morphologically-informed word embeddings
performs best in the downstream task of Xhosa-English translation.
