Restoration of fragmentary Babylonian texts using recurrent neural networks.
Ethan FetayaYonatan LifshitzElad AaronShai GordinPublished in: Proceedings of the National Academy of Sciences of the United States of America (2020)
The main sources of information regarding ancient Mesopotamian history and culture are clay cuneiform tablets. Many of these tablets are damaged, leading to missing information. Currently, the missing text is manually reconstructed by experts. We investigate the possibility of assisting scholars, by modeling the language using recurrent neural networks and automatically completing the breaks in ancient Akkadian texts from Achaemenid period Babylonia.