$\mathcal{E}$ KÚ [MASK]: Integrating Yorùbá cultural greetings into machine translation

This paper investigates the performance of massively multilingual neural machine translation (NMT) systems in translating Yor\`ub\'a greetings ($\mathcal{E}$ 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.