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Yannis Assael, Thea Sommerschield, Brendan Shillingford, Mahyar Bordbar, John Pavlopoulos, Marita Chatzipanagiotou, Ion Androutsopoulos, JONATHAN R. W. PRAG, Nando de Freitas
. However, over the centuries, many inscriptions have been damaged to the point of illegibility, transported far from their original location and their date of writing is steeped in uncertainty. Here we present Ithaca, a deep neural network for the textual restoration, geographical attribution and chronological attribution of ancient Greek inscriptions. Ithaca is designed to assist and expand the historian's workflow. The architecture of Ithaca focuses on collaboration, decision support and interpretability. While Ithaca alone achieves 62% accuracy when restoring damaged texts, the use of Ithaca by historians improved their accuracy from 25% to 72%, confirming the synergistic effect of this research tool. Ithaca can attribute inscriptions to their original location with an accuracy of 71% and can date them to less than 30 years of their ground-truth ranges, redating key texts of Classical Athens and contributing to topical debates in ancient history. This research shows how models such as Ithaca can unlock the cooperative potential between artificial intelligence and historians, transformationally impacting the way that we study and write about one of the most important periods in human history.
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Assael, Y., Sommerschield, T., Shillingford, B., Bordbar, M., Pavlopoulos, J., Chatzipanagiotou, M., Androutsopoulos, I., PRAG, J. R. W., & Freitas, N. D. (2022). Restoring and attributing ancient texts using deep neural networks. Nature, 603(7900), 280–283. https://doi.org/10.1038/s41586-022-04448-z
Assael, Yannis, et al. "Restoring and attributing ancient texts using deep neural networks." Nature, vol. 603, no. 7900, 2022, pp. 280–283, https://doi.org/10.1038/s41586-022-04448-z.
Assael, Yannis, Thea Sommerschield, Brendan Shillingford, Mahyar Bordbar, John Pavlopoulos, Marita Chatzipanagiotou, Ion Androutsopoulos, JONATHAN R. W. PRAG, and Nando de Freitas. "Restoring and attributing ancient texts using deep neural networks." Nature 603, no. 7900 (2022): 280–283. https://doi.org/10.1038/s41586-022-04448-z.
Assael Y., Sommerschield T., Shillingford B. [и др.] Restoring and attributing ancient texts using deep neural networks // Nature. — 2022. — Vol. 603, № 7900. — P. 280–283. — URL: https://doi.org/10.1038/s41586-022-04448-z
@article{assael2022restoring,
title = {{Restoring and attributing ancient texts using deep neural networks}},
author = {Yannis Assael and Thea Sommerschield and Brendan Shillingford and Mahyar Bordbar and John Pavlopoulos and Marita Chatzipanagiotou and Ion Androutsopoulos and JONATHAN R. W. PRAG and Nando de Freitas},
journal = {Nature},
year = {2022},
volume = {603},
number = {7900},
pages = {280--283},
doi = {10.1038/s41586-022-04448-z},
url = {https://doi.org/10.1038/s41586-022-04448-z}
}TY - JOUR AU - Assael, Yannis AU - Sommerschield, Thea AU - Shillingford, Brendan AU - Bordbar, Mahyar AU - Pavlopoulos, John AU - Chatzipanagiotou, Marita AU - Androutsopoulos, Ion AU - PRAG, JONATHAN R. W. AU - Freitas, Nando de TI - Restoring and attributing ancient texts using deep neural networks JO - Nature VL - 603 IS - 7900 SP - 280 EP - 283 PY - 2022 DA - 2022/03/09 DO - 10.1038/s41586-022-04448-z UR - https://doi.org/10.1038/s41586-022-04448-z ER -
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