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

• URL: http://arxiv.org/abs/2303.17972v2
• Date: Mon, 24 Apr 2023 16:29:38 GMT
• Title: $\varepsilon$ K\'U <MASK>: Integrating Yor\`ub\'a cultural greetings into machine translation
• Authors: Idris Akinade, Jesujoba Alabi, David Adelani, Clement Odoje and Dietrich Klakow
• Abstract summary: We present IkiniYorub'a, a Yorub'a-English translation dataset containing some Yorub'a greetings, and sample use cases. We show that different multilingual NMT systems including Google and NLLB struggle to accurately translate Yorub'a greetings into English. In addition, we trained a Yorub'a-English model by finetuning an existing NMT model on the training split of IkiniYorub'a and this achieved better performance when compared to the pre-trained multilingual NMT models
• Score: 14.469047518226708
• License: http://creativecommons.org/licenses/by/4.0/
• 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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