miscellaneous-8

Ancient Text Restoration

3260 papers • 126 benchmarks • 313 datasets

Image credit: Restoring and attributing ancient texts using deep neural networks

(Image credit: Papersgraph)

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Most implemented papers

Restoring and attributing ancient texts using deep neural networks

Yannis Assael, John Pavlopoulos, Thea Sommerschield, Brendan Shillingford, Mahyar Bordbar, Marita Chatzipanagiotou, I. Androutsopoulos, J. Prag, Nando de Freitas•Mon Feb 28 2022

Ithaca—a deep neural network for textual restoration, geographical attribution and dating of ancient Greek inscriptions—collaboratively aids historians’ study of damaged texts shows how models such as Ithaca can unlock the cooperative potential between artificial intelligence and historians.

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Blank Language Models

T. Jaakkola, R. Barzilay, T. Shen, Victor Quach•Fri Feb 07 2020

Experiments on style transfer and damaged ancient text restoration demonstrate the potential of the Blank Language Model, a model that generates sequences by dynamically creating and filling in blanks, for a wide range of applications.

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Restoring ancient text using deep learning: a case study on Greek epigraphy

Yannis Assael, Thea Sommerschield, J. Prag•Sun Oct 13 2019

Pythia is presented, the first ancient text restoration model that recovers missing characters from a damaged text input using deep neural networks, and sets the state-of-the-art inAncient text restoration.

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