TY  - JOUR
T1  - Building a Parallel Corpus and Training Translation Models Between Luganda and English
AU  - Kimera, Richard 
AU  - Rim, Daniela N. 
AU  - Choi, Heeyoul 
JO  - Journal of KIISE, JOK
PY  - 2022
DA  - 2022/1/14
DO  - 10.5626/JOK.2022.49.11.1009
KW  - luganda
KW  - neural machine translation
KW  - transformer
KW  - hyper-parameter
AB  - Recently, neural machine translation (NMT) which has achieved great successes needs 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.