YAD: Leveraging T5 for Improved Automatic Diacritization of Yor\`ub\'a Text

Autor:	Olawole, Akindele Michael, Alabi, Jesujoba O., Sakpere, Aderonke Busayo, Adelani, David I.
Rok vydání:	2024
Předmět:	Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	In this work, we present Yor\`ub\'a automatic diacritization (YAD) benchmark dataset for evaluating Yor\`ub\'a diacritization systems. In addition, we pre-train text-to-text transformer, T5 model for Yor\`ub\'a and showed that this model outperform several multilingually trained T5 models. Lastly, we showed that more data and larger models are better at diacritization for Yor\`ub\'a Comment: Accepted at AfricaNLP Workshop at ICLR 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.20218 Zobrazit plný text záznamu View this record from Arxiv