  Neural machine translation (NMT) has achieved great successes with large
datasets, so NMT is more premised on high-resource languages. This continuously
underpins the low resource languages such as Luganda due to the lack of
high-quality parallel corpora, so even 'Google translate' does not serve
Luganda at the time of this writing. In this paper, we build a parallel corpus
with 41,070 pairwise sentences for Luganda and English which is based on three
different open-sourced corpora. Then, we train NMT models with hyper-parameter
search on the dataset. Experiments gave us a BLEU score of 21.28 from Luganda
to English and 17.47 from English to Luganda. Some translation examples show
high quality of the translation. We believe that our model is the first
Luganda-English NMT model. The bilingual dataset we built will be available to
the public.
