Lecture image placeholder

Premium content

Access to this content requires a subscription. You must be a premium user to view this content.

Monthly subscription - $9.99Pay per view - $4.99Access through your institutionLogin with Underline account
Need help?
Contact us
Lecture placeholder background
VIDEO DOI: https://doi.org/10.48448/zrpb-8s64

workshop paper

ACL 2024

August 15, 2024

Bangkok, Thailand

Deep Learning Meets Egyptology: a Hieroglyphic Transformer for Translating Ancient Egyptian

keywords:

egyptology

middle egyptian

ancient languages

low resource machine translation

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.

Downloads

SlidesTranscript English (automatic)

Next from ACL 2024

Neural Lemmatization and POS-tagging models for Coptic, Demotic and Earlier Egyptian
workshop paper

Neural Lemmatization and POS-tagging models for Coptic, Demotic and Earlier Egyptian

ACL 2024

Aleksi Sahala and 1 other author

15 August 2024

Stay up to date with the latest Underline news!

Select topic of interest (you can select more than one)

PRESENTATIONS

  • All Lectures
  • For Librarians
  • Resource Center
  • Free Trial
Underline Science, Inc.
1216 Broadway, 2nd Floor, New York, NY 10001, USA

© 2023 Underline - All rights reserved