Abstract
In this work, we explore the use of Neural Machine Translation models for Toki Pona, a small, low resourced, and minimalistic language. We tackle the problem by employing the transformer model and transfer learning. Despite the challenges posed by the language’s limited resources and the scarcity of available data, we demonstrate that a high-accuracy machine translator can be developed for Toki Pona using these advanced techniques. Through transfer learning from existing models, we were able to adapt to the unique characteristics of Toki Pona and overcome the challenges of limited data. Our results show the potential for applying these methods to other under-resourced languages, providing a valuable tool for increasing accessibility and communication in these communities.
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Acknowledgements
Pablo Baggetto is supported by ValgrAI - Valencian Graduate School and Research Network for Artificial Intelligence, and Generalitat Valenciana under a grant for predoctoral studies.
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Baggetto, P., López, D., Larriba, A.M. (2023). Study and Automatic Translation of Toki Pona. In: Pertusa, A., Gallego, A.J., Sánchez, J.A., Domingues, I. (eds) Pattern Recognition and Image Analysis. IbPRIA 2023. Lecture Notes in Computer Science, vol 14062. Springer, Cham. https://doi.org/10.1007/978-3-031-36616-1_52
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