Restoration of fragmentary Babylonian texts using recurrent neural networks.

ABSTRACT: 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.

SUBMITTER: Fetaya E 

PROVIDER: S-EPMC7502733 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature