varepsilon K'U <MASK>: Integrating Yor`ub'a cultural greetings into machine translation

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.