  Stemming from the limited availability of datasets and textual resources for
low-resource languages such as isiZulu, there is a significant need to be able
to harness knowledge from pre-trained models to improve low resource machine
translation. Moreover, a lack of techniques to handle the complexities of
morphologically rich languages has compounded the unequal development of
translation models, with many widely spoken African languages being left
behind. This study explores the potential benefits of transfer learning in an
English-isiZulu translation framework. The results indicate the value of
transfer learning from closely related languages to enhance the performance of
low-resource translation models, thus providing a key strategy for low-resource
translation going forward. We gathered results from 8 different language
corpora, including one multi-lingual corpus, and saw that isiXhosa-isiZulu
outperformed all languages, with a BLEU score of 8.56 on the test set which was
better from the multi-lingual corpora pre-trained model by 2.73. We also
derived a new coefficient, Nasir's Geographical Distance Coefficient (NGDC)
which provides an easy selection of languages for the pre-trained models. NGDC
also indicated that isiXhosa should be selected as the language for the
pre-trained model.
