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

Geographical Distance Is The New Hyperparameter: A Case Study Of Finding The Optimal Pre-trained Language For English-isiZulu Machine Translation