Varepsilon kú mask: Integrating Y orùbá cultural greetings into machine translation (2023)

Abstract: This paper investigates the performance of massively multilingual neural machine translation (NMT) systems in translating Yorùbá greetings (kú mask), which are a big part of Yorùbá language and culture, into English. To evaluate these models, we present IkiniYorùbá, a Yorùbá-English translation dataset containing some Yorùbá 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 Yorùbá greetings into English. In addition, we trained a Yorùbá-English model by fine-tuning an existing NMT model on the training split of IkiniYorùbá and this achieved better performance when compared to the pre-trained multilingual NMT models, although they were trained on a large volume of data.

## introduction in recent years, multilingual neural machine translation (nmt) models have shown remarkable improvement in translating both high and lowresource languages and have become widely used in various applications @xcite @xcite . despite this progress, nmt models still struggle to accurately translate idiomatic expressions @xcite , cultural concepts such as proverbs @xcite , and common greetings, particularly in african languages like yorùbá-a west african language, which has a rich cultural heritage. an examination of nllb and google translate's model outputs reveals that they all fail to produce accurate translations for the input sentence. one possible explanation for this is the lack of sufficient training data including these types of greetings, even though they were trained on a large volume of multilingual data. furthermore, kú, a common word in these kinds of greetings, has two main interpretations that could mean either death or a compliment, depending on the context. similarly, the syntactic frame of occurrence also determines the meaning of the verb (the type of complement and adjunct), and this is due to the ambiguous nature of yorùbá verbs. hence, it is possible that these models were trained on data with kú having the meaning death. to address this issue, this paper introduces a new dataset dubbed ikiniyorùbá, a yorùbá-english translation dataset of popular yorùbá greetings. we evaluate the performance of existing multilingual nmt systems on this dataset, and the results demonstrate that although current multilingual nmt systems are good at translating yorùbá sentences into english, they struggle to accurately translate yorùbá greetings, highlighting the need for further research in translating such cultural concepts on low-resource african languages. ## yorùbá cultural greetings yorùbá is a language spoken by the yorùbá people. it is native to nigeria, benin and togo with an estimate of over 40 million speakers @xcite . yorùbá makes use of 25 latin letters excluding the latin characters (c, q, v, x and z), and additional letters (e . , gb, s . , o . ). yorùbá is a tonal language with three tones: low, middle and high. these tones are represented by the grave (e.g. "à "), optional macron (e.g. "ā") and acute (e.g. "á") accents respectively. greetings are inseparable from the yorùbá people since they are important for first impressions and are even considered to be a part of yorùbá identity. after the abolition of the slave trade at the beginning of the 19th century, the yorùbá indigenes who were rescued by the british warship settled in freetown, a place in present-day sierra leone. people began to call them a kú which is a fragment attached to all forms of greetings in yorùbá @xcite . this is because while an english speaker will say good morning, happy birthday, merry christmas, and so on, the yorùbá people would say e . káàrò . , e . kú o . jó . ìbí, and e . kú o . dún kérésìmesì. the recurrence of e . kú in their everyday conversation resulted the appellation a kú. e . kú has the same semantic importance as 'good-', 'merry-' and 'happy-' in english greetings. without the fragment e . kú in the communication frame of greeting, the cultural knowledge shared by interlocutors will be lost. structurally, e . kú can be syntactically explained to have a subject-predicate relationship, rather than being a single lexeme or a prefix as claimed by most scholars. using the paradigmatic relationship @xcite lens, e . can be replaced with any pronoun or nominal item (as described by interlocutors) with +human feature and still fit in perfectly. the +human feature is necessary because compliments are mainly for humans and kú requires a selection restriction to sieve out the non-human elements. kú on the other hand is a transitive predicate that requires a compliment. this compliment could either be a noun that signifies time like àárò . (morning), a noun that denotes season like ò . ririn/òtútù (cold), a noun that points to a celebration like kérésìmesì (christmas), a nominalized verb that describes an event or action like ìjókòó (sitting), and many more. omitting the compliment in a greeting construction will alter the interpretation of the expression which may also change the meaning of kú to death. ## related work the development of machine translation systems for low-resource languages such as yorùbá has seen a significant amount of research efforts in recent years. one major area of focus has been on curating translation datasets for these languages, which are collected using either automatic or manual methods. examples of automatically collected datasets that include yorùbá are jw300 @xcite , ccmatrix @xcite , and ccaligned @xcite . on the other hand, examples of manually translated datasets for yoruba include menyo-20k @xcite , mafand-mt @xcite , flores-101 @xcite , and ntrex @xcite . these datasets have been instrumental in the study, development, and improvement of machine translation systems for yorùbá. for example, @xcite investigated how domain data quality and the use of diacritics, a crucial aspect of yorùbá orthography, impact yorùbá-english translations. @xcite examined the effectiveness of yorùbá-english machine translation in translating bare nouns (bn), by comparing the results obtained from using statistical machine translation methods and neural approaches. @xcite investigated how to effectively leverage pre-trained models for translation of african languages including yorùbá. de-spite the attempts to create datasets and develop translation systems for yorùbá, to the best of our knowledge, only @xcite has examined a cultural aspect of yorùbá by evaluating their models on yorùbá proverbs, which are a significant part of yorùbá tradition. however, this research has not looked into how these models perform on another cultural aspect which is yorùbá greetings. furthermore, there appear to be no prior works that have evaluated machine translation performance specifically for this aspect of the language and for other languages. therefore, in this work, we investigate the performance of yorùbá-english translation models on yorùbá greetings. ## ikiniyorùbá corpus greetings dataset: we introduce ikiniyorùbá, a yorùbá-english translation dataset for yorùbá greetings and their usage in various contexts, containing 960 parallel instances. the data curation process involved three key stages. firstly, we gathered commonly used yorùbá greetings that cover a variety of situations such as time, season, celebration, and more, as outlined in section 2, resulting in a total of 160 yorùbá greetings. secondly, we created 5 different example sentences for each greeting, where the greetings are used in context, by native speakers of the language, resulting in 800 use cases in total. lastly, we asked an expert translator to translate the seed data and the use cases into english. we split the created data into train/dev/test splits with 100/20/40 seed greeting instances. for each instance in a split, the 5 example sentences created are assigned to the same split. conversational dataset: for our experiments, we used the movie transcripts subset of the menyo-20k @xcite ) dataset, which is a human-translated english-yorùbá dataset for movie transcripts. we selected this dataset because it consists of conversational data. ## conclusion in this study, we analyzed the performance of machine translation models in translating yorùbá greetings into english. to achieve this objective, we introduced a novel dataset called ikiniyorùbá, which contains a collection of yorùbá greetings and their respective sentence use cases. we evaluated three publicly available machine translation models on this dataset and found that, despite their ability to translate other yorùbá texts, they failed to accurately translate yorùbá greetings, which are a crucial aspect of yorùbá culture. in future research, we aim to expand the ikiniyorùbá dataset by adding more profession-based greetings and exploring ways to enhance the performance of machine translation models with these data.