3260 papers • 126 benchmarks • 313 datasets
Image credit: Restoring and attributing ancient texts using deep neural networks
(Image credit: Papersgraph)
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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.
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.
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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