Low Resource Neural Machine Translation: A Benchmark for Five African Languages

Abstract

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.

Accepted for AfricaNLP workshop at ICLR 2020

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Cited by in corpus (5)

• Low-resource Languages: A Review of Past Work and Future Challenges
• Benchmarking Multimodal AutoML for Tabular Data with Text Fields
• Integrating Unsupervised Data Generation into Self-Supervised Neural Machine Translation for Low-Resource Languages
• ChrEn: Cherokee-English Machine Translation for Endangered Language Revitalization
• IndoNLG: Benchmark and Resources for Evaluating Indonesian Natural Language Generation