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.

研究了最近神经机器翻译 (NMT) 在英语和五种非洲低资源语言 (LRL) 对之间的效果，并表明多语言模型 (multilingual approach) 在某些翻译方向上有 +5 分数的提升。同时，提供标准的实验数据和测试集以供未来的研究使用。