@inproceedings{moape-etal-2024-developing,
    title = "Developing Bilingual {E}nglish-Setswana Datasets for Space Domain",
    author = "Moape, Tebatso G.  and
      Ojo, Sunday Olusegun  and
      Olugbara, Oludayo O.",
    editor = "Mabuya, Rooweither  and
      Matfunjwa, Muzi  and
      Setaka, Mmasibidi  and
      van Zaanen, Menno",
    booktitle = "Proceedings of the Fifth Workshop on Resources for African Indigenous Languages @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.rail-1.4",
    pages = "32--36",
    abstract = "In the current digital age, languages lacking digital presence face an imminent risk of extinction. In addition, the absence of digital resources poses a significant obstacle to the development of Natural Language Processing (NLP) applications for such languages. Therefore, the development of digital language resources contributes to the preservation of these languages and enables application development. This paper contributes to the ongoing efforts of developing language resources for South African languages with a specific focus on Setswana and presents a new English-Setswana bilingual dataset that focuses on the space domain. The dataset was constructed using the expansion method. A subset of space domain English synsets from Princeton WordNet was professionally translated to Setswana. The initial submission of translations demonstrated an accuracy rate of 99{\%} before validation. After validation, continuous revisions and discussions between translators and validators resulted in a unanimous agreement, ultimately achieving a 100{\%} accuracy rate. The final version of the resource was converted into an XML format due to its machine-readable framework, providing a structured hierarchy for the organization of linguistic data.",
}

In the current digital age, languages lacking digital presence face an imminent risk of extinction. In addition, the absence of digital resources poses a significant obstacle to the development of Natural Language Processing (NLP) applications for such languages. Therefore, the development of digital language resources contributes to the preservation of these languages and enables application development. This paper contributes to the ongoing efforts of developing language resources for South African languages with a specific focus on Setswana and presents a new English-Setswana bilingual dataset that focuses on the space domain. The dataset was constructed using the expansion method. A subset of space domain English synsets from Princeton WordNet was professionally translated to Setswana. The initial submission of translations demonstrated an accuracy rate of 99{\%} before validation. After validation, continuous revisions and discussions between translators and validators resulted in a unanimous agreement, ultimately achieving a 100{\%} accuracy rate. The final version of the resource was converted into an XML format due to its machine-readable framework, providing a structured hierarchy for the organization of linguistic data.
Moape, Tebatso G. Ojo, Sunday Olusegun Olugbara, Oludayo O.
Developing Bilingual English-Setswana Datasets for Space Domain