Low Resource Neural Machine Translation: A Benchmark for Five African Languages
This work addresses the challenge of machine translation for under-resourced African languages, providing a benchmark and dataset for future research, though it is incremental as it builds on existing methods.
The authors benchmarked neural machine translation for five low-resource African languages (SATOS) against English, showing that multilingual modeling achieved the largest performance gains, with up to +5 BLEU points in six out of ten translation directions.
Recent advents in Neural Machine Translation (NMT) have shown improvements in low-resource language (LRL) translation tasks. In this work, we benchmark NMT between English and five African LRL pairs (Swahili, Amharic, Tigrigna, Oromo, Somali [SATOS]). We collected the available resources on the SATOS languages to evaluate the current state of NMT for LRLs. Our evaluation, comparing a baseline single language pair NMT model against semi-supervised learning, transfer learning, and multilingual modeling, shows significant performance improvements both in the En-LRL and LRL-En directions. In terms of averaged BLEU score, the multilingual approach shows the largest gains, up to +5 points, in six out of ten translation directions. To demonstrate the generalization capability of each model, we also report results on multi-domain test sets. We release the standardized experimental data and the test sets for future works addressing the challenges of NMT in under-resourced settings, in particular for the SATOS languages.