A Nature research paper suggests that a deep neural network trained to restore ancient Greek texts can do so with 72% accuracy when used by historians
is represented as 5 decades of equal 20% probability, whereas an inscription dated to 305Experimental evaluation
To compare performance in the three epigraphic tasks, we use four methods. First, we evaluate the difficulty of the restoration task by assigning two evaluators with epigraphical expertise a set of damaged inscriptions to restore, using the training set to search for textual parallels. Second, we provide the human experts with a ranked list of Ithaca’s top 20 restoration hypotheses to inform their predictions , therefore assessing the true impact of our work as a cooperative research aid.
Indeed, to demonstrate Ithaca’s creative potential, we applied our model to a contemporary dispute concerning the dating of a group of inscriptions whose interpretation is central to the political history of classical Athens. Historians disagree on whether these decrees should pre- or post-date 446/5
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