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arXiv · 2303.17972

$\varepsilon$ K\'U : Integrating Yor\`ub\'a cultural greetings into machine translation

Idris Akinade·Jesujoba Alabi·David Adelani·Clement Odoje·Dietrich Klakow
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Abstract

This paper investigates the performance of massively multilingual neural machine translation (NMT) systems in translating Yor\`ub\'a greetings ($\varepsilon$ k\'u [MASK]), which are a big part of Yor\`ub\'a language and culture, into English. To evaluate these models, we present IkiniYor\`ub\'a, a Yor\`ub\'a-English translation dataset containing some Yor\`ub\'a greetings, and sample use cases. We analysed the performance of different multilingual NMT systems including Google and NLLB and show that these models struggle to accurately translate Yor\`ub\'a greetings into English. In addition, we trained a Yor\`ub\'a-English model by finetuning an existing NMT model on the training split of IkiniYor\`ub\'a and this achieved better performance when compared to the pre-trained multilingual NMT models, although they were trained on a large volume of data.

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Idris Akinade, Jesujoba Alabi, David Adelani, Clement Odoje, Dietrich Klakow. 2023-03-31. $\varepsilon$ K\'U : Integrating Yor\`ub\'a cultural greetings into machine translation. https://arxiv.org/abs/2303.17972

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