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

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