Mattia De Cao


2024

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Deep Learning Meets Egyptology: a Hieroglyphic Transformer for Translating Ancient Egyptian
Mattia De Cao | Nicola De Cao | Angelo Colonna | Alessandro Lenci
Proceedings of the 1st Workshop on Machine Learning for Ancient Languages (ML4AL 2024)

This work explores the potential of Transformer models focusing on the translation of ancient Egyptian hieroglyphs. We present a novel Hieroglyphic Transformer model, built upon the powerful M2M-100 multilingual translation framework and trained on a dataset we customised from the Thesaurus Linguae Aegyptiae database. Our experiments demonstrate promising results, with the model achieving significant accuracy in translating hieroglyphics into both German and English. This work holds significant implications for Egyptology, potentially accelerating the translation process and unlocking new research approaches.