  Low-resource African languages have not fully benefited from the progress in
neural machine translation because of a lack of data. Motivated by this
challenge we compare zero-shot learning, transfer learning and multilingual
learning on three Bantu languages (Shona, isiXhosa and isiZulu) and English.
Our main target is English-to-isiZulu translation for which we have just 30,000
sentence pairs, 28% of the average size of our other corpora. We show the
importance of language similarity on the performance of English-to-isiZulu
transfer learning based on English-to-isiXhosa and English-to-Shona parent
models whose BLEU scores differ by 5.2. We then demonstrate that multilingual
learning surpasses both transfer learning and zero-shot learning on our
dataset, with BLEU score improvements relative to the baseline
English-to-isiZulu model of 9.9, 6.1 and 2.0 respectively. Our best model also
improves the previous SOTA BLEU score by more than 10.
