Building a Parallel Corpus and Training Translation Models Between Luganda and English
This work addresses the problem of low-resource language translation for Luganda speakers, though it is incremental as it applies existing methods to new data.
The authors tackled the lack of machine translation for Luganda by building a parallel corpus of 41,070 sentences and training neural machine translation models, achieving BLEU scores of 21.28 (Luganda to English) and 17.47 (English to Luganda).
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.