Deep Learning Meets Egyptology: a Hieroglyphic Transformer for Translating A ncient E gyptian (2024)

Abstract: 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.

## introduction egyptology, with its rich trove of texts and inscriptions, has recently begun to embrace the potential of computational linguistics. however, a notable scarcity of publications on the topic is evident, with existing efforts primarily focused on optical recognition of hieroglyphs rather than their translation @xcite . notably, the development of these resources primarily originates from computer science disciplines and highlights the need for deeper integration with egyptology field. we bridge this gap by proposing an egyptologydriven automatic translation approach, merging *mattia de cao made the most significant contributions to this study, including developing the study conception and its elaboration, designing the experiments, mining/analyzing the data and writing the manuscript. nicola de cao supported the implementation of computational models, created the paper layout, and revised the manuscript. angelo colonna reviewed the human evaluation process and ancient egyptian background material. alessandro lenci contributed to widening the automatic evaluation phase and provided technical review. egyptology with natural language processing (nlp) tools. our hieroglyphic transformer translates ancient egyptian using an adaptation of m2m-100 multilingual model @xcite to address hieroglyphic writing's challenges. we construct a meticulously curated dataset derived from the renowned database project thesaurus linguae aegyptiae (tla; richter et al., 2023) foot_0 ensuring its compatibility with the model through rigorous data filtering, cleaning and structuring. experiments yield promising results, with the hieroglyphic transformer achieving reasonable accuracy in translating hieroglyphs into both german and english. furthermore, we evaluate the model's performance on texts of varying grammatical complexity and literary styles, highlighting its capacity to handle diverse linguistic structures. this work holds significant implications for egyptology. nlp-powered approaches like ours can potentially accelerate and improve translation accuracy and depth. furthermore, it paves the way for applying deep learning models to decipher and translate other ancient languages. the main contributions of our work can be summarised as follows: ## dataset construction this work is based on a snapshot @xcite foot_2 collected from the database that also feeds the thesaurus linguae aegyptiae (tla; richter et al., 2023) 4 and last updated in 2018. ## conclusions we publicly released our dataset and source code and designed them for easy utilization and assessment. the ai model produces suitable results for research applications and is user-friendly. this work opens up avenues for future research, including expanding the dataset by incorporating other language phases (late egyptian, demotic and coptic), integrating additional modern languages , and conducting more comprehensive and diversified experiments. these efforts could pave the way for enhanced model precision and contribute significantly to the advancement of research in egyptology and the application of nlp to the translation and study of ancient languages.