Abstract
Ancient history relies on disciplines such as epigraphy—the study of inscribed texts known as inscriptions—for evidence of the thought, language, society and history of past civilizations1. 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.
Highlights
We present Ithaca, a deep neural network architecture trained to simultaneously perform the tasks of textual restoration, geographical attribution and chronological attribution
Working with Greek inscriptions To train Ithaca, we developed a pipeline to retrieve the unprocessed Packard Humanities Institute (PHI)[19,20] dataset, which consists of the transcribed texts of 178,551 inscriptions
Ithaca is to our knowledge the first epigraphic restoration and attribution model of its kind
Summary
Whereas Ithaca may have outperformed historians in the first baseline, the combination of a historian’s own (contextual) knowledge alongside Ithaca’s assistive input resulted in an even greater improvement over the model’s performance This collaborative potential is augmented by Ithaca’s design decisions, and by the different visualization aids increasing the interpretability of outputs, enabling historians to evaluate multiple hypotheses. Whereas the I.PHI labels are on average 27 years off the ‘lower’ dating proposed by modern re-evaluations, Ithaca’s predictions are on average only 5 years off the newly proposed ground truths This example eloquently illustrates how models such as Ithaca can contribute to key methodological debates on the chronological reorganization of Athenian imperialism, one of the most important moments in Greek history. Historians may use Ithaca’s interpretability-augmenting aids (such as saliency maps) to examine these predictions further and bring more clarity to Athenian history
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