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

• URL: http://arxiv.org/abs/2003.14402v1
• Date: Tue, 31 Mar 2020 17:50:07 GMT
• Title: Low Resource Neural Machine Translation: A Benchmark for Five African Languages
• Authors: Surafel M. Lakew, Matteo Negri, Marco Turchi
• Abstract summary: We benchmark NMT between English and five African LRL pairs (Swahili, Amharic, Tigrigna, Oromo, Somali) We compare a baseline single language pair NMT model against semi-supervised learning, transfer learning, and multilingual modeling. In terms of averaged BLEU score, the multilingual approach shows the largest gains, up to +5 points, in six out of ten translation directions.
• Score: 14.97774471012222
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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