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Restoring and attributing ancient texts using deep neural networks

Humanities

Restoring and attributing ancient texts using deep neural networks

Y. Assael, T. Sommerschield, et al.

Discover Ithaca, a groundbreaking deep neural network that revolutionizes the restoration of ancient Greek inscriptions. Developed by a team of experts including Yannis Assael and Ion Androutsopoulos, it enhances historians' accuracy and significantly contributes to understanding the past.

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Playback language: English
Abstract
This paper introduces Ithaca, a deep neural network designed to restore damaged ancient Greek inscriptions, attribute them geographically, and chronologically. Ithaca's architecture prioritizes collaboration with historians, decision support, and interpretability. When used by historians, Ithaca improved their accuracy in text restoration from 25% to 72%, achieving 62% accuracy independently. It also demonstrated 71% accuracy in geographical attribution and dated inscriptions to within 30 years of their actual dates, impacting key historical debates.
Publisher
Nature
Published On
Mar 10, 2022
Authors
Yannis Assael, Thea Sommerschield, Brendan Shillingford, Mahyar Bordbar, John Pavlopoulos, Marita Chatzipanagiotou, Ion Androutsopoulos, Jonathan Prag, Nando de Freitas
Tags
ancient Greek inscriptions
deep neural network
text restoration
geographical attribution
historical accuracy
interpretability
historical debates

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